{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习纳米学位\n",
    "## 监督学习\n",
    "## 项目2: 为*CharityML*寻找捐献者"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "欢迎来到机器学习工程师纳米学位的第二个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**'练习'**开始的标题表示接下来的代码部分中有你必须要实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以'TODO'标出。请仔细阅读所有的提示！\n",
    "\n",
    "除了实现代码外，你还必须回答一些与项目和你的实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。我们将根据你对问题的回答和撰写代码所实现的功能来对你提交的项目进行评分。\n",
    ">**提示：**Code 和 Markdown 区域可通过**Shift + Enter**快捷键运行。此外，Markdown可以通过双击进入编辑模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始\n",
    "\n",
    "在这个项目中，你将使用1994年美国人口普查收集的数据，选用几个监督学习算法以准确地建模被调查者的收入。然后，你将根据初步结果从中选择出最佳的候选算法，并进一步优化该算法以最好地建模这些数据。你的目标是建立一个能够准确地预测被调查者年收入是否超过50000美元的模型。这种类型的任务会出现在那些依赖于捐款而存在的非营利性组织。了解人群的收入情况可以帮助一个非营利性的机构更好地了解他们要多大的捐赠，或是否他们应该接触这些人。虽然我们很难直接从公开的资源中推断出一个人的一般收入阶层，但是我们可以（也正是我们将要做的）从其他的一些公开的可获得的资源中获得一些特征从而推断出该值。\n",
    "\n",
    "这个项目的数据集来自[UCI机器学习知识库](https://archive.ics.uci.edu/ml/datasets/Census+Income)。这个数据集是由Ron Kohavi和Barry Becker在发表文章_\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_之后捐赠的，你可以在Ron Kohavi提供的[在线版本](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf)中找到这个文章。我们在这里探索的数据集相比于原有的数据集有一些小小的改变，比如说移除了特征`'fnlwgt'` 以及一些遗失的或者是格式不正确的记录。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 探索数据\n",
    "运行下面的代码单元以载入需要的Python库并导入人口普查数据。注意数据集的最后一列`'income'`将是我们需要预测的列（表示被调查者的年收入会大于或者是最多50,000美元），人口普查数据中的每一列都将是关于被调查者的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass education_level  education-num       marital-status  \\\n",
       "0   39          State-gov       Bachelors           13.0        Never-married   \n",
       "1   50   Self-emp-not-inc       Bachelors           13.0   Married-civ-spouse   \n",
       "2   38            Private         HS-grad            9.0             Divorced   \n",
       "3   53            Private            11th            7.0   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race    sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White   Male        2174.0   \n",
       "1     Exec-managerial         Husband   White   Male           0.0   \n",
       "2   Handlers-cleaners   Not-in-family   White   Male           0.0   \n",
       "3   Handlers-cleaners         Husband   Black   Male           0.0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country income  \n",
       "0           0.0            40.0   United-States  <=50K  \n",
       "1           0.0            13.0   United-States  <=50K  \n",
       "2           0.0            40.0   United-States  <=50K  \n",
       "3           0.0            40.0   United-States  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(45222, 14)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为这个项目导入需要的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # 允许为DataFrame使用display()\n",
    "\n",
    "# 导入附加的可视化代码visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# 为notebook提供更加漂亮的可视化\n",
    "%matplotlib inline\n",
    "\n",
    "# 导入人口普查数据\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "# 成功 - 显示第一条记录\n",
    "display(data.head(n=4))\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：数据探索\n",
    "首先我们对数据集进行一个粗略的探索，我们将看看每一个类别里会有多少被调查者？并且告诉我们这些里面多大比例是年收入大于50,000美元的。在下面的代码单元中，你将需要计算以下量：\n",
    "\n",
    "- 总的记录数量，`'n_records'`\n",
    "- 年收入大于50,000美元的人数，`'n_greater_50k'`.\n",
    "- 年收入最多为50,000美元的人数 `'n_at_most_50k'`.\n",
    "- 年收入大于50,000美元的人所占的比例， `'greater_percent'`.\n",
    "\n",
    "**提示：** 您可能需要查看上面的生成的表，以了解`'income'`条目的格式是什么样的。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of records: 45222\n",
      "Individuals making more than $50,000: 11208\n",
      "Individuals making at most $50,000: 34014\n",
      "Percentage of individuals making more than $50,000: 24.78%\n"
     ]
    }
   ],
   "source": [
    "# TODO：总的记录数\n",
    "n_records = data.shape[0]\n",
    "\n",
    "# TODO：被调查者的收入大于$50,000的人数\n",
    "n_greater_50k = data[data['income'] == '>50K'].shape[0]\n",
    "\n",
    "# TODO：被调查者的收入最多为$50,000的人数\n",
    "n_at_most_50k = data[data['income'] == '<=50K'].shape[0]\n",
    "\n",
    "# TODO：被调查者收入大于$50,000所占的比例\n",
    "greater_percent = float (n_greater_50k) / n_records\n",
    "\n",
    "# 打印结果\n",
    "print \"Total number of records: {}\".format(n_records)\n",
    "print \"Individuals making more than $50,000: {}\".format(n_greater_50k)\n",
    "print \"Individuals making at most $50,000: {}\".format(n_at_most_50k)\n",
    "print \"Percentage of individuals making more than $50,000: {:.2f}%\".format(greater_percent * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 准备数据\n",
    "在数据能够被作为输入提供给机器学习算法之前，它经常需要被清洗，格式化，和重新组织 - 这通常被叫做**预处理**。幸运的是，对于这个数据集，没有我们必须处理的无效或丢失的条目，然而，由于某一些特征存在的特性我们必须进行一定的调整。这个预处理都可以极大地帮助我们提升几乎所有的学习算法的结果和预测能力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 转换倾斜的连续特征\n",
    "\n",
    "一个数据集有时可能包含至少一个靠近某个数字的特征，但有时也会有一些相对来说存在极大值或者极小值的不平凡分布的的特征。算法对这种分布的数据会十分敏感，并且如果这种数据没有能够很好地规一化处理会使得算法表现不佳。在人口普查数据集的两个特征符合这个描述：'`capital-gain'`和`'capital-loss'`。\n",
    "\n",
    "运行下面的代码单元以创建一个关于这两个特征的条形图。请注意当前的值的范围和它们是如何分布的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'workclass', 'education_level', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZsEwaiy+In5JpIrYYVnXxCtt3Yh+/7e5T03cKfUv3yJuJ9CzIjinNxfO2BZZz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\nmZmZmeXmBoSZmZmZmeX2/wGmtbH/hGlACgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e71c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将数据切分成特征和对应的标签\n",
    "income_raw = data['income']\n",
    "features_raw = data.drop('income', axis = 1)\n",
    "print list(data.columns)\n",
    "# 可视化原来数据的倾斜的连续特征\n",
    "vs.distribution(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于高度倾斜分布的特征如`'capital-gain'`和`'capital-loss'`，常见的做法是对数据施加一个<a href=\"https://en.wikipedia.org/wiki/Data_transformation_(statistics)\">对数转换</a>，将数据转换成对数，这样非常大和非常小的值不会对学习算法产生负面的影响。并且使用对数变换显著降低了由于异常值所造成的数据范围异常。但是在应用这个变换时必须小心：因为0的对数是没有定义的，所以我们必须先将数据处理成一个比0稍微大一点的数以成功完成对数转换。\n",
    "\n",
    "运行下面的代码单元来执行数据的转换和可视化结果。再次，注意值的范围和它们是如何分布的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EbX/xhfJm6f+SD/3qgnHExcWk7LYi+h9vlT5zNzAsXQQXrN/ZBab7Jw4kLub+VmL6Ymb2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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12689198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对于倾斜的数据使用Log转换\n",
    "skewed = ['capital-gain', 'capital-loss']\n",
    "features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# 可视化经过log之后的数据分布\n",
    "vs.distribution(features_raw, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过上面的两组图，可以看到Skewed数据在对数变换前后到底发生了什么：特征的取值范围从100000和4000分别下降到了12和8。\n",
    "\n",
    "做对比参照的是两个相对不倾斜的特征（hours-per-week和education-num）的分布特性。可以发现，过于倾斜的数据会导致有值的那一小部分会挤在很小的一块，剩下的绝大多数都是空白。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 规一化数字特征\n",
    "除了对于高度倾斜的特征施加转换，对数值特征施加一些形式的缩放通常会是一个好的习惯。在数据上面施加一个缩放并不会改变数据分布的形式（比如上面说的'capital-gain' or 'capital-loss'）；但是，规一化保证了每一个特征在使用监督学习器的时候能够被平等的对待。注意一旦使用了缩放，观察数据的原始形式不再具有它本来的意义了，就像下面的例子展示的。\n",
    "\n",
    "运行下面的代码单元来规一化每一个数字特征。我们将使用[`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)来完成这个任务。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.30137</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.02174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass education_level  education-num  marital-status  \\\n",
       "0  0.30137   State-gov       Bachelors            0.8   Never-married   \n",
       "\n",
       "      occupation    relationship    race    sex  capital-gain  capital-loss  \\\n",
       "0   Adm-clerical   Not-in-family   White   Male       0.02174           0.0   \n",
       "\n",
       "   hours-per-week  native-country  \n",
       "0        0.397959   United-States  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'age', u'workclass', u'education_level', u'education-num',\n",
      "       u'marital-status', u'occupation', u'relationship', u'race', u'sex',\n",
      "       u'capital-gain', u'capital-loss', u'hours-per-week', u'native-country'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 导入sklearn.preprocessing.StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 初始化一个 scaler，并将它施加到特征上\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "features_raw[numerical] = scaler.fit_transform(data[numerical])\n",
    "\n",
    "# 显示一个经过缩放的样例记录\n",
    "display(features_raw.head(n = 1))\n",
    "print features_raw.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：数据预处理\n",
    "\n",
    "从上面的**数据探索**中的表中，我们可以看到有几个属性的每一条记录都是非数字的。通常情况下，学习算法期望输入是数字的，这要求非数字的特征（称为类别变量）被转换。转换类别变量的一种流行的方法是使用**独热编码**方案。独热编码为每一个非数字特征的每一个可能的类别创建一个_“虚拟”_变量。例如，假设`someFeature`有三个可能的取值`A`，`B`或者`C`，。我们将把这个特征编码成`someFeature_A`, `someFeature_B`和`someFeature_C`.\n",
    "\n",
    "|   | 一些特征 |                    | 特征_A | 特征_B | 特征_C |\n",
    "| :-: | :-: |                            | :-: | :-: | :-: |\n",
    "| 0 |  B  |  | 0 | 1 | 0 |\n",
    "| 1 |  C  | ----> 独热编码 ----> | 0 | 0 | 1 |\n",
    "| 2 |  A  |  | 1 | 0 | 0 |\n",
    "\n",
    "此外，对于非数字的特征，我们需要将非数字的标签`'income'`转换成数值以保证学习算法能够正常工作。因为这个标签只有两种可能的类别（\"<=50K\"和\">50K\"），我们不必要使用独热编码，可以直接将他们编码分别成两个类`0`和`1`，在下面的代码单元中你将实现以下功能：\n",
    " - 使用[`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies)对`'features_raw'`数据来施加一个独热编码。\n",
    " - 将目标标签`'income_raw'`转换成数字项。\n",
    "   - 将\"<=50K\"转换成`0`；将\">50K\"转换成`1`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 total features after one-hot encoding.\n"
     ]
    }
   ],
   "source": [
    "# TODO：使用pandas.get_dummies()对'features_raw'数据进行独热编码\n",
    "features = pd.get_dummies(features_raw)\n",
    "\n",
    "# TODO：将'income_raw'编码成数字值\n",
    "# 特别注意：需要确保income中的元素是可以运算的数据类型\n",
    "# 如果写成 income[income == '>50K'] = 1, income[income == '<=50K'] = 0\n",
    "# 由于Series的dtype只会随着元素更新而自动往宽泛变化（例如int→object），并不会自动往小变（object→int）\n",
    "# 因此即使赋值的数据类型的确是int，但是由于income的dtype一开始就是object，因此更新元素后，其dtype依然是object\n",
    "# 这会导致无法运算。解决方案是显式调用 astype(int)，将Series的dtype转型为可运算的类型。\n",
    "# 反之，如果使用判断语句，则生成的新Series的dtype将会自动确定为bool，这是可以直接运算的。\n",
    "# income = income_raw.copy()       # 这里其实并不需要拷贝。因为(income == '>50K')这个语句只会生成一个全新的Series，而不会修改原DataFrame\n",
    "income = (income_raw == '>50K')    # 这样得到的income是bool元素类型的Series\n",
    "# income.astype(int)               # 这样得到的income是int类型（0，1）的Series\n",
    "\n",
    "\n",
    "# 打印经过独热编码之后的特征数量\n",
    "encoded = list(features.columns)\n",
    "print \"{} total features after one-hot encoding.\".format(len(encoded))\n",
    "\n",
    "# 移除下面一行的注释以观察编码的特征名字\n",
    "# print encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 混洗和切分数据\n",
    "现在所有的 _类别变量_ 已被转换成数值特征，而且所有的数值特征已被规一化。和我们一般情况下做的一样，我们现在将数据（包括特征和它们的标签）切分成训练和测试集。其中80%的数据将用于训练和20%的数据用于测试。\n",
    "\n",
    "运行下面的代码单元来完成切分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 36177 samples.\n",
      "Testing set has 9045 samples.\n"
     ]
    }
   ],
   "source": [
    "# 导入 train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 将'features'和'income'数据切分成训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, income, test_size = 0.2, random_state = 0)\n",
    "\n",
    "# 显示切分的结果\n",
    "print \"Training set has {} samples.\".format(X_train.shape[0])\n",
    "print \"Testing set has {} samples.\".format(X_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 评价模型性能\n",
    "在这一部分中，我们将尝试四种不同的算法，并确定哪一个能够最好地建模数据。这里面的三个将是你选择的监督学习器，而第四种算法被称为一个*朴素的预测器*。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评价方法和朴素的预测器\n",
    "*CharityML*通过他们的研究人员知道被调查者的年收入大于\\$50,000最有可能向他们捐款。因为这个原因*CharityML*对于准确预测谁能够获得\\$50,000以上收入尤其有兴趣。这样看起来使用**准确率**作为评价模型的标准是合适的。另外，把*没有*收入大于\\$50,000的人识别成年收入大于\\$50,000对于*CharityML*来说是有害的，因为他想要找到的是有意愿捐款的用户。这样，我们期望的模型具有准确预测那些能够年收入大于\\$50,000的能力比模型去**查全**这些被调查者*更重要*。我们能够使用**F-beta score**作为评价指标，这样能够同时考虑查准率和查全率：\n",
    "\n",
    "$$ F_{\\beta} = (1 + \\beta^2) \\cdot \\frac{precision \\cdot recall}{\\left( \\beta^2 \\cdot precision \\right) + recall} $$\n",
    "\n",
    "\n",
    "尤其是，当$\\beta = 0.5$的时候更多的强调查准率，这叫做**F$_{0.5}$ score** （或者为了简单叫做F-score）。\n",
    "\n",
    "通过查看不同类别的数据分布（那些最多赚\\$50,000和那些能够赚更多的），我们能发现：很明显的是很多的被调查者年收入没有超过\\$50,000。这点会显著地影响**准确率**，因为我们可以简单地预测说*“这个人的收入没有超过\\$50,000”*，这样我们甚至不用看数据就能做到我们的预测在一般情况下是正确的！做这样一个预测被称作是**朴素的**，因为我们没有任何信息去证实这种说法。通常考虑对你的数据使用一个*朴素的预测器*是十分重要的，这样能够帮助我们建立一个模型的表现是否好的基准。那有人说，使用这样一个预测是没有意义的：如果我们预测所有人的收入都低于\\$50,000，那么*CharityML*就不会有人捐款了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1 - 朴素预测器的性能\n",
    "*如果我们选择一个无论什么情况都预测被调查者年收入大于\\$50,000的模型，那么这个模型在这个数据集上的准确率和F-score是多少？*  \n",
    "**注意：** 你必须使用下面的代码单元将你的计算结果赋值给`'accuracy'` 和 `'fscore'`，这些值会在后面被使用，请注意这里不能使用scikit-learn，你需要根据公式自己实现相关计算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "针对整个数据集\n",
      "Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]\n",
      "针对测试数据集\n",
      "Naive Predictor: [Accuracy score: 0.2438, F-score: 0.2872]\n"
     ]
    }
   ],
   "source": [
    "def naive_model(y, beta):\n",
    "    total = y.shape[0]\n",
    "    true_positive = y[y == True].shape[0]\n",
    "    false_positive = total - true_positive\n",
    "    false_negative = 0\n",
    "    true_negative = 0\n",
    "\n",
    "    # TODO： 计算准确率\n",
    "    accuracy = float(true_positive) / total\n",
    "    # TODO： 使用上面的公式，并设置beta=0.5计算F-score\n",
    "    precision = float(true_positive) / (true_positive + false_positive)\n",
    "    recall = float(true_positive) / (true_positive + false_negative)\n",
    "    fscore = (1 + beta**2) * precision * recall / (precision * beta**2 + recall)\n",
    "    # 打印结果\n",
    "    print \"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore)\n",
    "    return accuracy, fscore\n",
    "\n",
    "print \"针对整个数据集\"\n",
    "naive_model(income, 0.5)\n",
    "\n",
    "print \"针对测试数据集\"\n",
    "accuracy, fscore = naive_model(y_test, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 监督学习模型\n",
    "**下面的监督学习模型是现在在** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **中你能够选择的模型**\n",
    "\n",
    "Model | Performance\n",
    ":---  | :---\n",
    "高斯朴素贝叶斯 (GaussianNB) | **Poor**\n",
    "决策树                     | **OK**\n",
    "集成方法 (Bagging, AdaBoost, Random Forest, Gradient Boosting) | **Great**\n",
    "K近邻 (KNeighbors)         | **OK (But slow in predicton)**\n",
    "随机梯度下降分类器 (SGDC)    | **OK**\n",
    "支持向量机 (SVM)            | **OK（But slow in training）**\n",
    "Logistic Regression        | **Great**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2 - 模型应用\n",
    "\n",
    "列出从上面的监督学习模型中选择的三个适合我们这个问题的模型，你将在人口普查数据上测试这每个算法。对于你选择的每一个算法：\n",
    "\n",
    "- *描述一个该模型在真实世界的一个应用场景。（你需要为此做点研究，并给出你的引用出处）*\n",
    "- *这个模型的优势是什么？他什么情况下表现最好？*\n",
    "- *这个模型的缺点是什么？什么条件下它表现很差？*\n",
    "- *根据我们当前数据集的特点，为什么这个模型适合这个问题。*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答： **\n",
    "我在下面的测试流水线上把这里列出的一共10种模型（集成方法算4种）都测了一遍（random_state=0），按测试数据的精确度和F-Score排名的结果如下：\n",
    "\n",
    "    - 最好的前三名是AdaBoost、Gradient Boosting和Logistic Regression。\n",
    "    - K近邻和支持向量机这两个模型的性能还可以，但是主要问题是它们耗时都远远高于其他模型。K近邻的在100%训练集下预测需要25秒，SVM在100%训练集下训练需要83秒。\n",
    "    - 对于SGDC和DT：在训练集较大时，随机梯度下降和决策树的性能基本类似，但是在训练集较小（1%）时，决策树会明显好于随机梯度下降。\n",
    "    - GaussianNB的精确度和F-Score全场最差。\n",
    "\n",
    "鉴于AdaBoost和Gradient Boosting都属于集成学习这个大类，因此下面选择AdaBoost、Logistic Regression、Decision Tree这三个模型回答问题。\n",
    "\n",
    "### 1. Decision Tree\n",
    "\n",
    "- **真实应用场景**\n",
    "\n",
    "    - 区分哈勃望远镜所获取图像信息中的真实行星和宇宙射线。Salzburg将决策树应用于识别图像中的行星和宇宙射线，给予模型两千多个训练数据，这些数据标注了20种特征，并且给出了是行星还是宇宙射线的标签，训练后的模型可以通过9个节点的复杂度就做到95%的泛化精确率。\n",
    "\n",
    "\n",
    "- **优点**\n",
    "\n",
    "    1. 可解释性好。决策树只要画出来，就能自动说明哪些特征起到重要作用，特征是如何划分的，可以分析出很多不言自明的信息。\n",
    "    2. 自动隐含特征选择的理念。决策树的算法倾向于选择最重要的特征作为最上层的节点，这与特征选择的思路不谋而合。\n",
    "    3. 对于非线性模型的拟合性能好。相比于回归算法使用多项式方程拟合，决策树的思路完全不同，因此线性与否并不会影响决策树的性能。\n",
    "\n",
    "\n",
    "- **缺点**\n",
    "\n",
    "    1. 稳定性差。数据分布的微小变化都可能导致树结构的较大变化。\n",
    "    2. 不适合用来描述参数特别复杂的数据。\n",
    "    3. 容易过拟合。随着树深度的增大，容易对训练数据抠得太死，以至于过拟合几乎是必然的结局。需要手动控制树的深度。\n",
    "    4. 容易被攻击。虽然决策树整体上可以用很多特征，但是从决策的每一步来看，它本质上还是依靠于单个特征的，因此对于垃圾邮件分类的问题，垃圾邮件可以通过修改很少的特征就躲避开决策树分类器的识别。\n",
    "\n",
    "\n",
    "- **适用场景**\n",
    "\n",
    "    - 如果问题与特征之间存在着比较简单而清晰的树状关系（或者说理想的判决门限近似于矩形，与各个特征坐标平行）那么用决策树的效果最好。\n",
    "    - 决策树也可以用于帮助人们对手头的数据有一个初步的了解，比如哪个特征相对比较重要，为后续的工作提供一些信息。\n",
    "\n",
    "\n",
    "- **不适用的场景**\n",
    "\n",
    "    对于有很多很多重要性均等的特征（或者特征之间的关系耦合的比较复杂）的问题，决策树将会很不稳定。\n",
    "\n",
    "\n",
    "- **为什么适合于目前这个问题**\n",
    "    \n",
    "    因为需要可解释性比较强的模型。\n",
    "    \n",
    "    疑问：到底该如何分析“XXX算法适合XXX问题”呢？\n",
    "    \n",
    "\n",
    "- **Reference**: Page75, https://booksite.elsevier.com/9780124438804/leondes_expert_vol1_ch3.pdf\n",
    "\n",
    "\n",
    "### 2. Logistic Regression\n",
    "\n",
    "- **真实应用场景**\n",
    "    \n",
    "    - 广泛用于商业风险评估。例如J.-Y. KUNG的文章中使用LR对房贷进行风险评估。\n",
    "    \n",
    "    \n",
    "- **优点**\n",
    "\n",
    "    1. 不容易过拟合。因为LR本质上是线性分类器，其方差较小，模型也不复杂，不容易过拟合。\n",
    "    2. 输出值具有概率含义。\n",
    "    3. 运算量小，运行速度快。\n",
    "    \n",
    "\n",
    "- **缺点**\n",
    "    1. 本身并不能处理非线性可分的问题（但是可以通过Kernel Trick在高维进行线性划分）\n",
    "    2. 容易欠拟合（过于简化问题）\n",
    "    \n",
    "    \n",
    "- **适用场景**\n",
    "    \n",
    "    - 存在单个线性门限的解的问题。\n",
    "    - 需要运算速度快的场景\n",
    "\n",
    "\n",
    "- **不适用的场景**\n",
    "    \n",
    "    非线性可分的问题。需要用核函数在高维进行线性划分。\n",
    "    \n",
    "\n",
    "- **为什么适合于目前这个问题**\n",
    "\n",
    "    我是看测出来的评价指标打分挺高才选的...事实证明，实验本身就是判断模型是否适合当前问题的有力依据。\n",
    "\n",
    "\n",
    "- **Reference**: \n",
    "    - https://www.zhihu.com/question/21449767\n",
    "    - http://ir.csu.edu.tw/bitstream/987654321/3004/2/Application+of+Logistic+Regression+Analysis+of+Home+Mortgage+Loan+Prepayment+and+Default+Risk.pdf\n",
    "\n",
    "\n",
    "### 3. AdaBoost\n",
    "\n",
    "- **真实应用场景**\n",
    "\n",
    "    B. Markoski等人将AdaBoost用于识别视频中的篮球队员。\n",
    "    \n",
    "    \n",
    "- **优点**\n",
    "    \n",
    "    - 具有较低的泛化误差\n",
    "    - 可以使用绝大多数的分类器作为基学习器（注意：对于比较强的模型则需要先弱化才能用），而不是只拘泥于一种模型。\n",
    "    \n",
    "\n",
    "- **缺点**\n",
    "\n",
    "    - 对于异常值敏感\n",
    "    - 运行速度慢\n",
    "    \n",
    "    \n",
    "- **适用场景**\n",
    "\n",
    "    - 具有数值特征的问题\n",
    "    \n",
    "\n",
    "- **不适用的场景**\n",
    "\n",
    "    应该没有什么不适合的，如果说有，就是不适合特征特别多的问题，因为会导致运行速度很慢。因为AdaBoost其实底层用的是单节点决策树，因此即使对于一个样本，有多少个特征就要跑多少次决策树，更不要提还要迭代很多次。就现在这个问题，100个特征，500个学习器，就需要7秒钟。如果识别几十万个特征的一张图片，将会慢得多。但是这个问题可以通过特征选择来规避，所以严格来讲不算是问题。\n",
    "    \n",
    "\n",
    "- **为什么适合于目前这个问题**\n",
    "\n",
    "    我是看测出来的评价指标打分挺高才选的...当前问题的特征数量不算太多，对于运行时间没有特别要求，而且追求最高性能，所以AdaBoost挺好的。\n",
    "    \n",
    "\n",
    "- **Reference**: \n",
    "\n",
    "    - https://www.uni-obuda.hu/journal/Markoski_Ivankovic_Ratgeber_Pecev_Glusac_57.pdf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习 - 创建一个训练和预测的流水线\n",
    "为了正确评估你选择的每一个模型的性能，创建一个能够帮助你快速有效地使用不同大小的训练集并在测试集上做预测的训练和测试的流水线是十分重要的。\n",
    "你在这里实现的功能将会在接下来的部分中被用到。在下面的代码单元中，你将实现以下功能：\n",
    "\n",
    " - 从[`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics)中导入`fbeta_score`和`accuracy_score`。\n",
    " - 用样例训练集拟合学习器，并记录训练时间。\n",
    " - 用学习器来对训练集进行预测并记录预测时间。\n",
    " - 在最前面的300个*训练数据*上做预测。\n",
    " - 计算训练数据和测试数据的准确率。\n",
    " - 计算训练数据和测试数据的F-score。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO：从sklearn中导入两个评价指标 - fbeta_score和accuracy_score\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    model_name = learner.__class__.__name__\n",
    "    # TODO：使用sample_size大小的训练数据来拟合学习器\n",
    "    # TODO: Fit the learner to the training data using slicing with 'sample_size'\n",
    "    start = time() # 获得程序开始时间\n",
    "    learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "    end = time() # 获得程序结束时间\n",
    "    \n",
    "    # TODO：计算训练时间\n",
    "    results['train_time'] = end - start\n",
    "    # print model_name, 'train_time:', results['train_time']\n",
    "    \n",
    "    # TODO: 得到在测试集上的预测值\n",
    "    #       然后得到对前300个训练数据的预测结果\n",
    "    start = time() # 获得程序开始时间\n",
    "    pred_test = learner.predict(X_test)\n",
    "    pred_train = learner.predict(X_train[:300])\n",
    "    end = time() # 获得程序结束时间\n",
    "    \n",
    "    # TODO：计算预测用时\n",
    "    results['pred_time'] = end - start\n",
    "    # print model_name, 'pred_time:', results['pred_time']\n",
    "            \n",
    "    # TODO：计算在最前面的300个训练数据的准确率\n",
    "    results['acc_train'] = accuracy_score(y_train[:300], pred_train)\n",
    "    # print model_name, 'train accuracy:', results['acc_train']\n",
    "    \n",
    "    # TODO：计算在测试集上的准确率\n",
    "    results['acc_test'] = accuracy_score(y_test, pred_test)\n",
    "    print model_name, 'test accuracy:', results['acc_test']\n",
    "    \n",
    "    # TODO：计算在最前面300个训练数据上的F-score\n",
    "    results['f_train'] = fbeta_score(y_train[:300], pred_train, 0.5)\n",
    "    # print model_name, 'train F-Score:', results['f_train']\n",
    "        \n",
    "    # TODO：计算测试集上的F-score\n",
    "    results['f_test'] = fbeta_score(y_test, pred_test, 0.5)\n",
    "    print model_name, 'test F-Score:', results['f_test']\n",
    "       \n",
    "    # 成功\n",
    "    print \"{} trained on {} samples.\".format(model_name, sample_size)\n",
    "        \n",
    "    # 返回结果\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：初始模型的评估\n",
    "在下面的代码单元中，您将需要实现以下功能：             \n",
    "- 导入你在前面讨论的三个监督学习模型。             \n",
    "- 初始化三个模型并存储在`'clf_A'`，`'clf_B'`和`'clf_C'`中。         \n",
    "  - 如果可能对每一个模型都设置一个`random_state`。       \n",
    "  - **注意：**这里先使用每一个模型的默认参数，在接下来的部分中你将需要对某一个模型的参数进行调整。             \n",
    "- 计算记录的数目等于1%，10%，和100%的训练数据，并将这些值存储在`'samples'`中             \n",
    "\n",
    "**注意：**取决于你选择的算法，下面实现的代码可能需要一些时间来运行！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier test accuracy: 0.772139303483\n",
      "DecisionTreeClassifier test F-Score: 0.536351763169\n",
      "DecisionTreeClassifier trained on 361 samples.\n",
      "DecisionTreeClassifier test accuracy: 0.801658374793\n",
      "DecisionTreeClassifier test F-Score: 0.593874833555\n",
      "DecisionTreeClassifier trained on 3617 samples.\n",
      "DecisionTreeClassifier test accuracy: 0.818684355998\n",
      "DecisionTreeClassifier test F-Score: 0.628170832949\n",
      "DecisionTreeClassifier trained on 36177 samples.\n",
      "AdaBoostClassifier test accuracy: 0.820784964069\n",
      "AdaBoostClassifier test F-Score: 0.633010468643\n",
      "AdaBoostClassifier trained on 361 samples.\n",
      "AdaBoostClassifier test accuracy: 0.849861802101\n",
      "AdaBoostClassifier test F-Score: 0.70188208381\n",
      "AdaBoostClassifier trained on 3617 samples.\n",
      "AdaBoostClassifier test accuracy: 0.857600884467\n",
      "AdaBoostClassifier test F-Score: 0.724550898204\n",
      "AdaBoostClassifier trained on 36177 samples.\n",
      "LogisticRegression test accuracy: 0.810834715312\n",
      "LogisticRegression test F-Score: 0.608163745442\n",
      "LogisticRegression trained on 361 samples.\n",
      "LogisticRegression test accuracy: 0.836926478718\n",
      "LogisticRegression test F-Score: 0.674581160546\n",
      "LogisticRegression trained on 3617 samples.\n",
      "LogisticRegression test accuracy: 0.848313985627\n",
      "LogisticRegression test F-Score: 0.699293323489\n",
      "LogisticRegression trained on 36177 samples.\n",
      "GaussianNB test accuracy: 0.351796572692\n",
      "GaussianNB test F-Score: 0.310134346668\n",
      "GaussianNB trained on 361 samples.\n",
      "GaussianNB test accuracy: 0.367385295744\n",
      "GaussianNB test F-Score: 0.320790835402\n",
      "GaussianNB trained on 3617 samples.\n",
      "GaussianNB test accuracy: 0.608291873964\n",
      "GaussianNB test F-Score: 0.428112885072\n",
      "GaussianNB trained on 36177 samples.\n",
      "SGDClassifier test accuracy: 0.628081813156\n",
      "SGDClassifier test F-Score: 0.442724986145\n",
      "SGDClassifier trained on 361 samples.\n",
      "SGDClassifier test accuracy: 0.815588723051\n",
      "SGDClassifier test F-Score: 0.619313647247\n",
      "SGDClassifier trained on 3617 samples.\n",
      "SGDClassifier test accuracy: 0.822996130459\n",
      "SGDClassifier test F-Score: 0.635292281277\n",
      "SGDClassifier trained on 36177 samples.\n",
      "RandomForestClassifier test accuracy: 0.802763957988\n",
      "RandomForestClassifier test F-Score: 0.587211326448\n",
      "RandomForestClassifier trained on 361 samples.\n",
      "RandomForestClassifier test accuracy: 0.832283029298\n",
      "RandomForestClassifier test F-Score: 0.662204810099\n",
      "RandomForestClassifier trained on 3617 samples.\n",
      "RandomForestClassifier test accuracy: 0.837700386954\n",
      "RandomForestClassifier test F-Score: 0.671507260891\n",
      "RandomForestClassifier trained on 36177 samples.\n",
      "GradientBoostingClassifier test accuracy: 0.827086788281\n",
      "GradientBoostingClassifier test F-Score: 0.648989628996\n",
      "GradientBoostingClassifier trained on 361 samples.\n",
      "GradientBoostingClassifier test accuracy: 0.855831951354\n",
      "GradientBoostingClassifier test F-Score: 0.721371101069\n",
      "GradientBoostingClassifier trained on 3617 samples.\n",
      "GradientBoostingClassifier test accuracy: 0.863018242123\n",
      "GradientBoostingClassifier test F-Score: 0.73953385618\n",
      "GradientBoostingClassifier trained on 36177 samples.\n",
      "BaggingClassifier test accuracy: 0.808070757324\n",
      "BaggingClassifier test F-Score: 0.603052620344\n",
      "BaggingClassifier trained on 361 samples.\n",
      "BaggingClassifier test accuracy: 0.833941404091\n",
      "BaggingClassifier test F-Score: 0.665492581919\n",
      "BaggingClassifier trained on 3617 samples.\n",
      "BaggingClassifier test accuracy: 0.839248203427\n",
      "BaggingClassifier test F-Score: 0.675012518778\n",
      "BaggingClassifier trained on 36177 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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3nop1/cUyg0zNbGmi1vbFaiU46Un0NT4rEzzMSvS9bcqvNA9O/0/6wbWU/juIwbMvpPV1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\nzQqEJEmSpLpZgZAkSZJUt/8Ppr5mEBS464UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112725c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：从sklearn中导入三个监督学习模型\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# TODO：初始化三个模型（在这里尝试了上面列举的10种模型）\n",
    "clfs = []\n",
    "clfs.append(DecisionTreeClassifier(random_state=0))\n",
    "clfs.append(AdaBoostClassifier(random_state=0))\n",
    "clfs.append(LogisticRegression(random_state=0))\n",
    "clfs.append(GaussianNB())\n",
    "# clfs.append(KNeighborsClassifier())    # slow: prediction takes 25s\n",
    "clfs.append(SGDClassifier(random_state=0))\n",
    "# clfs.append(SVC())                     # too slow: training takes 83s\n",
    "clfs.append(RandomForestClassifier(random_state=0))\n",
    "clfs.append(GradientBoostingClassifier(random_state=0))\n",
    "clfs.append(BaggingClassifier(random_state=0))\n",
    "\n",
    "\n",
    "# TODO：计算1%， 10%， 100%的训练数据分别对应多少点. 注意传入的必须是整数，因此需要强制转型。\n",
    "samples_1 = int(y_train.shape[0] * 0.01)      # 361\n",
    "samples_10 = int(y_train.shape[0] * 0.1)      # 3617\n",
    "samples_100 = y_train.shape[0] * 1            # 36177\n",
    "\n",
    "# 输出学习器的结果：遍历10个模型\n",
    "results = {}\n",
    "for clf in clfs:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "\n",
    "# 选择三个模型存入dict供可视化\n",
    "result_for_display = {}\n",
    "result_for_display['DecisionTreeClassifier'] = results['DecisionTreeClassifier']\n",
    "result_for_display['LogisticRegression'] = results['LogisticRegression']\n",
    "result_for_display['AdaBoostClassifier'] = results['AdaBoostClassifier']\n",
    "        \n",
    "# 对选择的三个模型得到的评价结果进行可视化\n",
    "vs.evaluate(result_for_display, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 提高效果\n",
    "\n",
    "在这最后一节中，您将从三个有监督的学习模型中选择*最好的*模型来使用学生数据。你将在整个训练集（`X_train`和`y_train`）上通过使用网格搜索优化至少调节一个参数以获得一个比没有调节之前更好的F-score。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3 - 选择最佳的模型\n",
    "\n",
    "*基于你前面做的评价，用一到两段向*CharityML*解释这三个模型中哪一个对于判断被调查者的年收入大于\\$50,000是最合适的。*             \n",
    "**提示：**你的答案应该包括关于评价指标，预测/训练时间，以及该算法是否适合这里的数据的讨论。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：** 三者中最适合的是AdaBoost模型。原因如下：\n",
    "\n",
    "\n",
    "- **评价指标**\n",
    "\n",
    "    由于为了评估模型泛化的能力，主要以测试集的Accuracy和F-Score作为评价指标，可以看到，Logistic Regresson / AdaBoost / Decision Tree 这三个模型中，AdaBoost模型的Accuracy和F-Score在所有训练样本数量的情况下都是最高的，最高达到85.7%的准确度和72.4%的F-Score，超过相同环境下的Logistic Regression的准确度 1%、F-Score 3%，更超过相同环境下的决策树模型的精确度4%、F-Score 10%。\n",
    "\n",
    "\n",
    "- **时间**\n",
    "\n",
    "    虽然AdaBoost的评价指标最好，但是相对的训练耗时也最长，在100%训练集的时候达到1.4秒，而其他两种算法无论在那种训练集下的训练时间都不超过0.4秒。虽然AdaBoost的耗时较多，但是还在容忍范围内。\n",
    "\n",
    "\n",
    "- **是否适合数据**\n",
    "\n",
    "    AdaBoost本质上是决策树的升级版，因此性能更胜一筹，其泛化误差可以根据最大学习器数量的增加而进一步提升。相比之下，决策树随着树深度的增加将会不可避免的出现过拟合。另外，对于这套数据而言，AdaBoost和Logistic Regression同样使用网格搜索调参（分别对n_estimator，C进行调参），AdaBoost的F-Score可以突破75%，而LR最多只能达到70%，AdaBoost始终超过Logistic Regression。因此，在对于时间不是特别敏感的前提下，AdaBoost是三者中最适合的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4 - 用通俗的话解释模型\n",
    "\n",
    "*用一到两段话，向*CharityML*用外行也听得懂的话来解释最终模型是如何工作的。你需要解释所选模型的主要特点。例如，这个模型是怎样被训练的，它又是如何做出预测的。避免使用高级的数学或技术术语，不要使用公式或特定的算法名词。*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答： ** \n",
    "\n",
    "AdaBoost其实是基于其他分类算法之上的一种机制，它能够通过反复训练，让原本性能不是特别好的算法达到更高的性能。其思路可以类比于让学生准备考试，一个学生的单次学习能力有限，如果让这个学生反复去做那些他掌握的不好的题，不断查漏补缺，并且把从错题中学到的东西综合在一起，到最后他的水平一定会比一开始强。\n",
    "\n",
    "\n",
    "- **AdaBoost的训练过程**\n",
    "\n",
    "    具体来讲，AdaBoost会对训练数据进行多次训练。第一次训练时，所有的训练样本都具有同样的重要性。在第二次训练开始之前，我们会检查第一次训练的结果。首先要确保准确率比瞎猜强，如果还没有瞎猜强就说明当前用的模型根本不能再提升，需要换模型，如果比瞎猜强，我们会给样本中那些分类错误的样本（例如>50K的预测成<=50K）分配更重要的分值，然后用这个修改过的训练集对同样的模型进行第二次训练，如此循环往复，直到训练出N个模型。\n",
    "\n",
    "\n",
    "- **AdaBoost的预测过程**\n",
    "\n",
    "    AdaBoost会将输入的特征放入之前训练出来的N个模型中，得到N个预测值，然后最后根据每个模型的表现好坏，综合得出一个最终的预测值。\n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：模型调优\n",
    "调节选择的模型的参数。使用网格搜索（GridSearchCV）来至少调整模型的重要参数（至少调整一个），这个参数至少需给出并尝试3个不同的值。你要使用整个训练集来完成这个过程。在接下来的代码单元中，你需要实现以下功能：\n",
    "\n",
    "- 导入[`sklearn.model_selection.GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)和[`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).\n",
    "- 初始化你选择的分类器，并将其存储在`clf`中。\n",
    " - 如果能够设置的话，设置`random_state`。\n",
    "- 创建一个对于这个模型你希望调整参数的字典。\n",
    " - 例如: parameters = {'parameter' : [list of values]}。\n",
    " - **注意：** 如果你的学习器（learner）有 `max_features` 参数，请不要调节它！\n",
    "- 使用`make_scorer`来创建一个`fbeta_score`评分对象（设置$\\beta = 0.5$）。\n",
    "- 在分类器clf上用'scorer'作为评价函数运行网格搜索，并将结果存储在grid_obj中。\n",
    "- 用训练集（X_train, y_train）训练grid search object,并将结果存储在`grid_fit`中。\n",
    "\n",
    "**注意：** 取决于你选择的参数列表，下面实现的代码可能需要花一些时间运行！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **分析AdaBoost模型随n_estimators变化的趋势**\n",
    "\n",
    "在用GridSearch调参之前，我们首先需要确定要调的参数空间，以及模型性能和该参数之间的变化关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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EzMqAMoAePXpQUVHR4oZWVla2qpxkBvVf9lMfZj/1YfZTH2audAZrzTEduMPMFgALgX8B\nteGyU919jZkdCjxnZu+6+4vJFYRB3EyAkpISLy0tbXEjKioqaE05yQzqv+ynPsx+6sPspz7MXOkM\n1tYARyTM9wrT6rj7VmAygJkZ8D6wLFy2JvxeZ2ZPEpxWrResiYiIiLRn6bxmbS5wtJn1NbM8YAIw\nOzGDmRWHywAuB150961mVmRmncI8RcDpwNtpbKuIiIhIRkrbyJq715jZVOBZIAo84O7vmNmUcPkM\noD/wsJk58A7w5bB4D+DJYLCNHOBRd38mXW0VERERyVRpvWbN3ecAc5LSZiRM/xM4JkW5ZcCQdLZN\nREREJBvoDQYiIiIiGUzBmoiIiEgGU7AmIiIiksEUrImIiIhkMAVrIiIiIhlMwZqIiIhIBlOwJiIi\nIpLBFKyJiIiIZDAFayIiIiIZTMGaiIiISAZTsCYiIiKSwdIarJnZWDNbYmZLzeyGFMu7mtmTZvaW\nmb1uZgObW1ZERETkYJC2YM3MosBdwJnAAGCimQ1IynYjsMDdBwMXA3e0oKyIiIhIu5fOkbURwFJ3\nX+buu4BZwLikPAOAvwG4+7tAHzPr0cyyIiIiIu1eOoO1nsCqhPnVYVqiN4FzAcxsBNAb6NXMsiIi\nIiLtXs4BXv904A4zWwAsBP4F1LakAjMrA8oAevToQUVFRYsbUVlZ2apykhnUf9lPfZj91IfZT32Y\nudIZrK0BjkiY7xWm1XH3rcBkADMz4H1gGVDYVNmEOmYCMwFKSkq8tLS0xQ2tqKigNeUkM6j/sp/6\nMPupD7Of+jBzpfM06FzgaDPra2Z5wARgdmIGMysOlwFcDrwYBnBNlhURERE5GKRtZM3da8xsKvAs\nEAUecPd3zGxKuHwG0B942MwceAf4cmNl09VWERERkUyV1mvW3H0OMCcpbUbC9D+BY5pbVkRERORg\nozcYiIiIiGQwBWsiIiIiGUzBmoiIiEgGU7AmIiIiksEUrImIiIgAlJdDnz4QiQTf5eUHukWAgjUR\nkcyRoQeKNtPOt6/87q/Q5xs5RG42+nwjh/K7v3Kgm9Tm2vU2lpdTfttk+pyzgsh3nT7nrKD8tskZ\n8Ts90K+bEpG2Ul4ON90EK1fCkUfCtGkwadKBblWbKb/7K9y0bCYri2o5cnuUaf9RxqSrfnmgm9V2\nwgPFTefsZmUXOHLLCqbdNplJ0D76sT1snzvEYsF30qf83msp++heqjoGWVd0rKVszd1wxy4mTb51\nT/nE732ZTkO5/HXrYNWqBvOU/+YmyraW19/Gn25l0nk378mfuK7k9e7L8jaqy2OxvdKc4PuxX15F\n2Rm7qQof1b+iGMrO2A33fZVJB/g3qmBNDhrt+mDfHg6EcfEDYm1t8InFKL/vq5Stv7/+QeJn25k0\n/kdB/vhBND6d+EmVvi9pjaUnfmprm87nDrW1lP/hh6kPFHdczqS3395zsElcd0NpjS1rIr8nb5s7\neGJaQn6cWKyWWMwZsG0rOzp2IOa11OLEPEatx4jhxLyW39Uu5BtjnR25e7bvirG7WXfXlzjrjpvx\nsD53x4mF38F8/IDqsfC7Ll/we3F33GO47UmPH4A9/tmTmmIZdfXWtcET0yAesrgF08nf14+j7vcZ\nV5UL16+5n6IT7q+rw4169bV0Pp1l32pk+XdGQ1WH+tt4zaZyNn6xvNnb15z2tnbZvqxjxmeo+/ur\n2748uGnoRg70v6LmiRF0lispKfF58+a1uJzeh5bdmtN/5Xd/hbI1d1OVuyetw26Y2fOq1gdsiUFF\nTc2e4KIZ0757d/BdWwM1NXh8WfgdzNfgtbVYQv54Hq/dkz+2u4bf/v77TBlTvdc/NIW74fYX8vn8\niV8iVhvDa2uJeVCmNlZDLBaDWC2x2lrcY8Rqa4jFavFYLPwOPjH38DtMq40R81iQz2Nheiyow2Nh\n3hge8z3LPZh2j+fzuvy4UxuL4RaUcYNY+I9tzIJ/UL9+OmzsUL8LulbBD1/Y+8AJqQ+mjX1nQpny\nQVCZX38bi3bB2UuCfRGLGDXmxCJGzKA2AjEzYubUhml70sNpg1gkYbouP8QIl5FQJmHagdqI70m3\neL1eNx3fLpFMYoBh4bTVpSXPG3t+wDu9BlL8ns0hdnN6YiUzm+/uJU3l08iaABk46uQOu3bBjh17\nPtXVsGMHXlVFrGo7VFfjO6roseAtdr+zEK+qwndUsXtHJdVV26jeWUnVrip27qzkG4c/n/p/ve/f\nTc5pf6CGGLVeSw1BAFHjtdSYU+sxaolRiwd54tMWIwbURIIDW034qbWm0+LzzU1rsv4IcGb9Xbgj\nF648fSdw337osANjUweY+rn9sy4L/1lP/K5bZrZ3uiXmNcwS81uYP1xuwVTlri0p17s9F/5x4uFE\nLEKECBEzIhYNvyNhumEWIWoRLJyPL8sLvy3MH7UIFtZTr0wkGta1Zx1B3nh+I2rRsP3xskE9O7bU\n0Kk4Pygbrydsh0Ui/O+8O1MeCHH4fyd+s24f7rWPEvZruKP3pFnCgdgiwf6M15Eij1lCekK/1fWS\ngdmey7jrDujxbQyn99Sd0Ndm3PibMj7qEKu3eYdtj/DTix5Iam/C0E9dOyASrr+uhXVfSUGHWb2y\n9fJE9t7ZiWVT1QOw6b1NdPtUt4T9vnc7rpj5edZ0rL+NPSsjPDjlmbr1NFR/Ypvr2m0tb2e9/veG\n19US5z54Bh/E6v8dHpnbfZ/r3lcK1mTPqFPyKaa7CQI29yBQCoOl+MerqvDqHXVBEjt24Dt24Duq\niG3fzs7qbVRVbaW6ejs7dlayc1cV1buqqN69g+rdO9hRU83Omp1Ux3ZS7bupju2imt3s9Bp2WC07\nc6A6B3bEv3OD75RpK/ekxQqAguZt+/oimHDq2lbttxw3om5EMaJEyMGIeqRuPoqRY5FwOjiwRYmE\nadFg3oL5fIsSsQg5FiVqUSKRPd85kSjRSE6QFv+OJsxHc5jx/m8bPBB+87irg4Nx/EAQHpgJD94G\nWCRSd3Cty2sQsShA3cE+vhyjLiBIPLDG8xE/SGNYJH4gDQOM+Loj0bqD3taV2+napzMRi4bt2RM0\nQISpj1zIB0WpDxL3lf0JnLr6EveDYUQiew6Aex0A6rZ374NSYjCwV0CWtIPjp8dSHSTcvV7+eJnk\n/PGzGw0dKA6PduHxCb+va1/yQT5xO2DPAT/+jSVsa9im+D6JENlTT9LBsS5vUn11+yYpz9tz32bw\niMEN5nl8QTmraj+ut31H5HTj+lE31EtviJOeEY5662nhWSfv9Q+u/ejBeqP3PzriEj53zOcbLdsW\ngUZdXSn/IWieN9a8wbAjh+2pK6ldP+g9mWs+vL/eNv6g92SO73l8g/XurzN4+/rb+N5//Q/X/fka\ndviuurQOlse0s+/Y16bts7QGa2Y2FriD4GXs97n79KTlXYBfA0eGbfmZuz8YLlsObANqgZrmDBNK\nw7y6mtj6dcTWfYSvX0f1R2vYtG4lmzeu4b9rH0o56vSVlXfzrzPvoToS2ytASgySGkrbFf9ldQg/\nLZQXi5BPhALPIZ8c8i2HAssl33IpiObRMZJH92ge+TkF1MTy6NqxiNy8AvLyCoNPbj750XzycgrI\ny8njthd+zIbC+n/Ih22PcMv595ATzSFKhEgkJwiiIjl7giSLEo1Gg4ArkkM0Gg3/tx0JD77xg2L4\nv+Xgv9zBJLbnAB1fffx0WPwfsIQDcPLBt96BMn5ADg+ykUgQEP1xxnOsiW2ut329ol0pG3l93Xzd\nAbwZ86mClcR/vPfKm7SsXtkG/vcc98aWNxjeb3jK5WbGj3pPZura+geJH/a5jFOOPLXB9aRaV2N5\nGyvT4ChBG9U//ew7mfLU5VQlHSimn31n3YGwqRGLA2lJZAk9OvZocPlPxv2csicvq7d9Pxn3c7oW\ndt0fTUyrL1/zAAV3F2TWGYoWilq00b64bOp95N+dl9Xb2Jiy4WUU5RVx0/M3sXLLSo7sciTTxkxj\n0qADfcVaGoM1M4sCdwGnAauBuWY2290XJWS7Gljk7meZ2SeAJWZW7l731zza3Tekq43Zpja8FigW\nq6W2citVH6xk09plYdC1mk2b17J563o2b9/I1uotbK6pZEusii22i825tWwqhE0FsKmQuot8KWx4\nfVvz4ZcjoMBzyfccCsKgKQic8siP5HJIJI/8aB55Ofnk5RaQl1tAfl4HcvMKyS/oQG5uAbk5+eTn\n5JOXk09+NI/c8JOfk09eGHDlRfMoyC0gP6eA3Jy8YJQkPOVAJBi9iQc97l4X4EQtyvpF6zl80OFB\ncEOEnEhOXRATsQjRSJTu767ka+t/lfJ/vWcOPKf+qYQUQ/Cp5htb1tq8rfHTL/wi5YFw+hfu5Khu\nR7W63v0lalGKC4obXD756vvIa8cHCYAvDfkSkUgkIw8UbSG+He11+yA4EzGJ9vObTKW9b+OkQZMy\n8jeZzpG1EcBSd18GYGazgHFAYrDmQCcLjlIdgY+BmjS2qdXKF5bv8z8yMY/VBVy1XkttrJZtu7ax\nsXIDm9evZNNHy9m0fhWbN3/Ilq3r2FL1MVt2bGbLrm1siVWxlZ1sju5mS24tmwpgZ6re6xh+gE67\nI3SpzaHYO9CFQnrndGBgbhGd8rtQ1LErRZ0PoUPXQ7nj5Z+xvkP9UacjKiP8buqLewKm8PQWtmdU\nqO6amYRPNBINrq0JA6acSE69PPG0eFC092m21Gl1p9WSrkepeK+CIYcNaXTfX/nVR+h4d8d2e7DX\ngbB9yNQDRVtp79snki7pDNZ6AqsS5lcDJyTl+QUwG/gA6ASMd/f4hSkO/NXMaoF73H1mGtvaqPKF\n5XuNWqzYsoKyJy9jZ81Ozut/HpurN7NpxyY27tgYfLatY8vGNWz5eC1btq1ja+XHbKvewtZdlWyt\n3c4Wr2ZLZDdbcmrYHW14vRaBLjlQHItQXJNDcSyPw2o70plCOnkRnfI60bGwmKKirnToeiiFhxxG\nh2496NyhmI75nYnm5GLRKEQieDQCkSh50TyikSh5kTxyojnkRnL5xPvruH7dw/VPMfX9MoN6Dms0\noMrE0zENae8Hex0IRUTap7Q9usPMzgfGuvvl4fyXgBPcfWpSnlOArwFHAc8BQ9x9q5n1dPc1ZnZo\nmH6Nu7+YYj1lQBlAjx49hs+aNavFba2srKRjx44NLr+oYhxrbWu99Ei462KNxCvRGBRXQ9cd0LUa\nOu+O0qU2j87k08kK6RgpoiivE0V5xRQWFlNY1J38ToeQX9yDvK6fxAsLEy6aNjx+sXDdXVipg6eG\nTrU15PXnb+cXO/7I6o4xelVGmFp4FiPGXNdomUzRVP9J5lMfZj/1YfZTH+5/o0ePPuCP7lgDHJEw\n3ytMSzQZmO5BxLjUzN4H+gGvu/saAHdfZ2ZPEpxWrReshSNuMyF4zlprnpfW1HO6PqyoH6hB8Nyh\nb8/Np7MV0Dnagc65HSnK70RRYWc6FHWlqHN38rt0p/Y/urP7E93ZfeghWGEBHo1CNEJeTgHR3Dzy\ncgvIyQmu48qL5pETySEnkhOcTrQIUYvuNZ2O0azS0lK+2ea17h96Tl72Ux9mP/Vh9lMfZq50Bmtz\ngaPNrC9BkDYBuCgpz0pgDPCSmfUAPg0sM7MiIOLu28Lp04EfpLGtjTpyS/C07VTp5/zvn8nLySc3\nt4Cc3HxycwrIzcsnNyef3GhuymArfl2XiIiISFPSFqy5e42ZTQWeJXh0xwPu/o6ZTQmXzwB+CDxk\nZgsJTvR9y903mNl/AE+GI0g5wKPu/ky62tqUaQu6U3byxr2eDt9hF/x4QTeO7zfmQDVLREREDgJp\nfc6au88B5iSlzUiY/oBg1Cy53DKg8dv79qNJl98Bt03mppHx9y7CtJdymXT9zw9000RERKSd0xsM\nmmPSJCYBk266CVauhCOPhGnTsu8F2SIiIpJ1FKw116RJCs5ERERkv4s0nUVEREREDhQFayIiIiIZ\nrNnBmpmdamaTw+lPhI/kEBEREZE0alawZmbfA74FfDtMygV+na5GiYiIiEiguSNr5wBnA9uh7pEb\nndLVKBEREREJNDdY2xW+EsoBwrcKiIiIiEiaNTdYe9zM7gGKzewK4K/AvelrloiIiIhAM5+z5u4/\nM7PTgK0svLAgAAAb5UlEQVQE7+/8rrs/l9aWiYiIiEjTwZqZRYG/uvtoQAGaiIiIyH7U5GlQd68F\nYmbWZT+0R0REREQSNPeatUpgoZndb2Y/j3+aKmRmY81siZktNbMbUizvYmZ/NLM3zeyd+HPcmlNW\nRERE5GDQ3HeD/j78NFt4+vQu4DRgNTDXzGa7+6KEbFcDi9z9LDP7BLDEzMqB2maUFREREWn3mnuD\nwcNmlgccEyYtcffdTRQbASx192UAZjYLGAckBlwOdDIzAzoCHwM1wAnNKCsiIiLS7jUrWDOzUuBh\nYDlgwBFmdom7v9hIsZ7AqoT51QRBWKJfALOB+EN2x7t7zMyaUzbetjKgDKBHjx5UVFQ0Z5P2UllZ\n2apykhnUf9lPfZj91IfZT32YuZp7GvR/gdPdfQmAmR0DPAYM38f1nwEsAD4LHAU8Z2YvtaQCd58J\nzAQoKSnx0tLSFjeioqKC1pSTzKD+y37qw+ynPsx+6sPM1dwbDHLjgRqAu/+b4P2gjVkDHJEw3ytM\nSzQZ+L0HlgLvA/2aWVZERESk3WtusDbPzO4zs9Lwcy8wr4kyc4GjzaxveL3bBIJTnolWAmMAzKwH\nwQN3lzWzrIiIiEi719zToFcR3Ll5bTj/EvDLxgq4e42ZTQWeBaLAA+7+jplNCZfPAH4IPGRmCwmu\nhfuWu28ASFW2RVsmIiIi0g40N1jLAe5w91uh7rEc+U0Vcvc5wJyktBkJ0x8Apze3rIiIiMjBprmn\nQZ8HChPmCwle5i4iIiIiadTcYK3A3SvjM+F0h/Q0SURERETimhusbTezYfEZMysBdqSnSSIiIiIS\n19xr1q4DfmtmH4TznwTGp6dJIiIiIhLX6MiamR1vZoe5+1yC55/9BtgNPEPwTDQRERERSaOmToPe\nA+wKp08CbiR4wfomwrcGiIiIiEj6NHUaNOruH4fT44GZ7v4E8ISZLUhv00RERESkqZG1qJnFA7ox\nwN8SljX3ejcRERERaaWmAq7HgL+b2QaCuz9fAjCzTwFb0tw2ERERkYNeo8Gau08zs+cJ7v78i7t7\nuCgCXJPuxomIiIgc7Jo8lenur6ZI+3d6miMiIiIiiZr7UNxWMbOxZrbEzJaa2Q0pln/DzBaEn7fN\nrNbMuoXLlpvZwnDZvHS2U0RERCRTpe0mgfBl73cBpwGrgblmNtvdF8XzuPstwC1h/rOA6xPuPgUY\n7e4b0tVGERERkUyXzpG1EcBSd1/m7ruAWcC4RvJPJLihQURERERC6QzWegKrEuZXh2n1mFkHYCzw\nREKyA381s/lmVpa2VoqIiIhksEx5VtpZwD+SToGe6u5rzOxQ4Dkze9fdX0wuGAZyZQA9evSgoqKi\nxSuvrKxsVTnJDOq/7Kc+zH7qw+ynPsxc6QzW1gBHJMz3CtNSmUDSKVB3XxN+rzOzJwlOq9YL1tx9\nJuGrr0pKSry0tLTFDa2oqKA15SQzqP+yn/ow+6kPs5/6MHOl8zToXOBoM+trZnkEAdns5Exm1gUY\nBTyVkFZkZp3i08DpwNtpbKuIiIhIRkrbyJq715jZVOBZIAo84O7vmNmUcPmMMOs5BA/c3Z5QvAfw\npJnF2/iouz+TrraKiIiIZKq0XrPm7nOAOUlpM5LmHwIeSkpbBgxJZ9tEREREskFaH4orIiIiIvtG\nwZqIiIhIBlOwJiIiIpLBFKyJiIiIZDAFayIiIiIZTMGaiIiISAZTsCYiIiKSwRSsiYiIiGQwBWsi\nIiIiGUzBmoiIiEgGU7AmIiIiksEUrImIiIhksLQGa2Y21syWmNlSM7shxfJvmNmC8PO2mdWaWbfm\nlBURERE5GKQtWDOzKHAXcCYwAJhoZgMS87j7Le4+1N2HAt8G/u7uHzenrIiIiMjBIJ0jayOApe6+\nzN13AbOAcY3knwg81sqyIiIiIu1SThrr7gmsSphfDZyQKqOZdQDGAlNbUbYMKAPo0aMHFRUVLW5o\nZWVlq8pJZlD/ZT/1YfZTH2Y/9WHmSmew1hJnAf9w949bWtDdZwIzAUpKSry0tLTFK6+oqKA15SQz\nqP+yn/ow+6kPs5/6MHOl8zToGuCIhPleYVoqE9hzCrSlZUVERETarXSOrM0FjjazvgSB1gTgouRM\nZtYFGAV8saVlRURE2pvdu3ezevVqqqur9+t6u3TpwuLFi/frOg8WBQUF9OrVi9zc3FaVT1uw5u41\nZjYVeBaIAg+4+ztmNiVcPiPMeg7wF3ff3lTZdLVVREQkU6xevZpOnTrRp08fzGy/rXfbtm106tRp\nv63vYOHubNy4kdWrV9O3b99W1ZHWa9bcfQ4wJyltRtL8Q8BDzSkrIiLS3lVXV+/3QE3Sx8zo3r07\n69evb3UdeoOBiIhIhlGg1r7sa38qWBMREZE6GzduZOjQoQwdOpTDDjuMnj171s3v2rWrWXVMnjyZ\nJUuWNJrnrrvuory8vC2azFNPPcXQoUMZMmQIAwYM4L777muTejNFpjy6Q0RERFqjvBxuuglWroQj\nj4Rp02DSpFZX1717dxYsWADAzTffTMeOHfn617++Vx53x92JRFKP+Tz44INNrufqq69udRsT7dy5\nk6uuuop58+Zx+OGHs3PnTlasWLFPdTa1fftbZrRCREREWq68HMrKYMUKcA++y8qC9Da2dOlSBgwY\nwKRJkzj22GNZu3YtZWVllJSUcOyxx/KDH/ygLu+pp57KggULqKmpobi4mBtuuIEhQ4Zw0kknsW7d\nOgC+853vcPvtt9flv+GGGxgxYgSf/vSneeWVVwDYvn075513HgMGDOD888+npKSkLpCM27JlC+5O\nt27dAMjPz+eYY44B4MMPP2TcuHEMHjyYIUOG8NprrwHwP//zPwwcOJCBAwdy5513Nrh9Tz/9NCed\ndBLDhg1j/PjxbN++nQNBI2siIiKZ6rrrICk42curr8LOnXunVVXBl78M996buszQoRAGSS317rvv\n8sgjj1BSUgLA9OnT6datGzU1NYwePZrzzz+fAQP2fpX3li1bGDVqFNOnT+drX/saDzzwADfccEO9\nut2d119/ndmzZ/ODH/yAZ555hjvvvJPDDjuMJ554gjfffJNhw4bVK3fooYdyxhln0Lt3b8aMGcNZ\nZ53F+PHjiUQiXH311Zx22mlMnTqVmpoaqqqqeO211ygvL2fu3LnU1NQwYsQISktLKSws3Gv71q1b\nx/Tp03n++efp0KED06ZN44477uDGG29s1b7bFxpZExERyVbJgVpT6fvoqKOOqgvUAB577DGGDRvG\nsGHDWLx4MYsWLapXprCwkDPPPBOA4cOHs3z58pR1n3vuufXyvPzyy0yYMAGAIUOGcOyxx6Ys+9BD\nD/Hcc89RUlLC9OnTKSsrA4K3Mlx55ZUA5OTk0LlzZ15++WXOO+88CgsL6dSpE1/4whd46aWX6m3f\nK6+8wqJFizj55JMZOnQo5eXlDbY93TSyJiIikqmaGgHr0yc49Zmsd29Iw3s+i4qK6qbfe+897rjj\nDl5//XWKi4v54he/mPJBvnl5eXXT0WiUmpqalHXn5+c3macxgwcPZvDgwVx00UX079+/7iaDltyJ\nmbh97s7YsWP51a9+1eK2tDWNrImIiGSradOgQ4e90zp0CNLTbOvWrXTq1InOnTuzdu1ann322TZf\nxymnnMLjjz8OwMKFC1OO3G3dupUXX3yxbn7BggX07t0bgNGjRzNjRvB419raWrZu3crIkSN58skn\n2bFjB5WVlTz11FOMHDmyXr0nn3wyf//731m2bBkQXD/33nvvtfk2NodG1kRERLJV/K7PNrwbtLmG\nDRvGgAED6NevH7179+aUU05p83Vcc801XHzxxQwYMKDu06VLl73yuDs/+clPuOKKKygsLKRjx448\n8MADAPziF7/giiuu4J577iEnJ4d77rmHESNGMHHiRI4//ngArrrqKgYNGsTSpUv3qrdHjx7cf//9\njB8/vu6RJT/+8Y85+uij23w7m2Luvt9Xmi4lJSU+b968FperqKigtLS07Rsk+4X6L/upD7Of+rDt\nLF68mP79++/39Wbi66ZqamqoqamhoKCA9957j9NPP5333nuPnJzsG2tK1a9mNt/dSxooUif7tlZE\nREQOCpWVlYwZM4aamhrcvW6E7GCT1i02s7HAHQQvY7/P3aenyFMK3A7kAhvcfVSYvhzYBtQCNc2J\nPEVERKT9KC4uZv78+Qe6GQdc2oI1M4sCdwGnAauBuWY2290XJeQpBn4JjHX3lWZ2aFI1o919Q7ra\nKCIiIpLp0nk36Ahgqbsvc/ddwCxgXFKei4Dfu/tKAHdfl8b2iIiIiGSddAZrPYFVCfOrw7RExwBd\nzazCzOab2cUJyxz4a5helsZ2ioiIiGSsA32VXg4wHBgDFAL/NLNX3f3fwKnuviY8Nfqcmb3r7i8m\nVxAGcmUQ3GZb0YqHAFZWVraqnGQG9V/2Ux9mP/Vh2+nSpQvbtm3b7+utra09IOs9WFRXV7f+byT+\nZvm2/gAnAc8mzH8b+HZSnhuA7yfM3w9ckKKum4GvN7XO4cOHe2u88MILrSonmUH9l/3Uh9lPfdh2\nFi1adEDWu3XrVnd3Ly0t9WeeeWavZbfddptPmTKl0fJFRUXu7r5mzRo/77zzUuYZNWqUz507t9F6\nbrvtNt++fXvd/JlnnumbNm1qsv1Neffdd33UqFE+ZMgQ79evn19xxRX7XGdLpOpXYJ43I6ZK52nQ\nucDRZtbXzPKACcDspDxPAaeaWY6ZdQBOABabWZGZdQIwsyLgdODtNLZVREQkK5UvLKfP7X2IfD9C\nn9v7UL6wfJ/qmzhxIrNmzdorbdasWUycOLFZ5Q8//HB+97vftXr9t99+O1VVVXXzc+bMobi4uNX1\nxV177bVcf/31LFiwgMWLF3PNNdfsc521tbX7XEdzpC1Yc/caYCrwLLAYeNzd3zGzKWY2JcyzGHgG\neAt4neDxHm8DPYCXzezNMP3P7v5MutoqIiKSjcoXllP2xzJWbFmB46zYsoKyP5btU8B2/vnn8+c/\n/7nuqf3Lly/ngw8+YOTIkXXPPRs2bBiDBg3iqaeeqld++fLlDBw4EIAdO3YwYcIE+vfvzznnnMOO\nHTvq8l111VWUlJRw7LHH8r3vfQ+An//853zwwQeMHj2a0aNHA9CnTx82bAgeDHHrrbcycOBABg4c\nyO3he1OXL19O//79ueKKKzj22GM5/fTT91pP3Nq1a+nVq1fd/KBBg4Ag4Pr617/OwIEDGTx4MHfe\neScAzz//PMcddxyDBg3isssuY+fOnXXt+da3vsWwYcP47W9/y//93/8xduxYhg8fzsiRI3n33Xdb\nve8bktZr1tx9DjAnKW1G0vwtwC1JacuAIelsm4iISKa77pnrWPDhggaXv7r6VXbW7twrrWp3FV9+\n6svcO//elGWGHjaU28c2/IL4bt26MWLECJ5++mnGjRvHrFmzuPDCCzEzCgoKePLJJ+ncuTMbNmzg\nxBNP5Oyzz27wZel33303HTp0YPHixbz11lsMGzasbtm0adPo1q0btbW1jBkzhrfeeotrr72WW2+9\nlRdeeIFDDjlkr7rmz5/Pgw8+yGuvvYa7c8IJJzBq1Ci6du3Ke++9x2OPPca9997LhRdeyBNPPMEX\nv/jFvcpff/31fPazn+Xkk0/m9NNPZ/LkyRQXFzNz5kyWL1/OggULyMnJ4eOPP6a6uppLL72U559/\nnmOOOYaLL76Yu+++m+uuuw6A7t2788YbbwAwZswYZsyYwdFHH81rr73GV77yFf72t781uH9bQy9y\nFxERyVLJgVpT6c2VeCo08RSou3PjjTcyePBg/vM//5M1a9bw0UcfNVjPiy++WBc0DR48mMGDB9ct\ne/zxxxk2bBjHHXcc77zzTsqXtCd6+eWXOeeccygqKqJjx46ce+65vPTSSwD07duXoUOHAjB8+HCW\nL19er/zkyZNZvHgxF1xwARUVFZx44ons3LmTv/71r1x55ZV1b0bo1q0bS5YsoW/fvhxzzDEAXHLJ\nJXu9LH78+PFAcGPNK6+8wgUXXMDQoUO58sorWbt2baPb0RoH+m5QERERaUBjI2AAfW7vw4otK+ql\n9+7Sm4pLK1q93nHjxnH99dfzxhtvUFVVxfDhwwEoLy9n/fr1zJ8/n9zcXPr06UN1dXWL63///ff5\n2c9+xty5c+natSuXXnppq+qJy8/Pr5uORqMpT4NCcD3dZZddxmWXXcbAgQN5++3WXQ5fVFQEQCwW\no7i4mAULGh79bAsaWRMREclS08ZMo0Nuh73SOuR2YNqYaftUb8eOHRk9ejSXXXbZXjcWbNmyhUMP\nPZTc3FxeeOEFVqyoHygm+sxnPsOjjz4KwNtvv81bb70FwNatWykqKqJLly589NFHPP3003VlOnXq\nlPIRIiNHjuQPf/gDVVVVbN++nSeffJKRI0c2e5ueeeYZdu/eDcCHH37Ixo0b6dmzJ6eddhr33HMP\nNTU1AHz88cd8+tOfZvny5SxduhSAX/3qV4waNapenZ07d6Zv37789re/BYKRxzfffLPZbWouBWsi\nIiJZatKgScw8aya9u/TGMHp36c3Ms2YyadCkfa574sSJvPnmm3sFa5MmTWLevHkMGjSIRx55hH79\n+jVax1VXXUVlZSX9+/fnu9/9bt0I3ZAhQzjuuOPo168fF110EaecckpdmbKyMsaOHVt3g0HcsGHD\nuPTSSxkxYgQnnHACl19+Occdd1yzt+cvf/kLAwcOZMiQIZxxxhnccsstHHbYYVx++eUceeSRDB48\nmCFDhvDoo49SUFDAgw8+yAUXXMCgQYOIRCJMmTIlZb3l5eXcf//9DBkyhGOPPTblTRf7yoLHfLQP\nJSUlPm/evBaXq6iooLS0tO0bJPuF+i/7qQ+zn/qw7SxevJj+/fvv9/Vu27aNTp067ff1HixS9auZ\nzXf3kqbKamRNREREJIMpWBMRERHJYArWRERERDKYgjUREZEM056uJ5d9708FayIiIhmkoKCAjRs3\nKmBrJ9ydjRs3UlBQ0Oo69FBcERGRDNKrVy9Wr17N+vXr9+t6q6ur9ymgkIYVFBTs9V7SlkprsGZm\nY4E7gCjBS9qnp8hTCtwO5AIb3H1Uc8uKiIi0N7m5ufTt23e/r7eioqJFzy2T/SdtwZqZRYG7gNOA\n1cBcM5vt7osS8hQDvwTGuvtKMzu0uWVFREREDgbpvGZtBLDU3Ze5+y5gFjAuKc9FwO/dfSWAu69r\nQVkRERGRdi+dwVpPYFXC/OowLdExQFczqzCz+WZ2cQvKioiIiLR7B/oGgxxgODAGKAT+aWavtqQC\nMysDysLZSjNb0op2HAJsaEU5yQzqv+ynPsx+6sPspz7c/3o3J1M6g7U1wBEJ873CtESrgY3uvh3Y\nbmYvAkPC9KbKAuDuM4GZ+9JQM5vXnHdzSWZS/2U/9WH2Ux9mP/Vh5krnadC5wNFm1tfM8oAJwOyk\nPE8Bp5pZjpl1AE4AFjezrIiIiEi7l7aRNXevMbOpwLMEj994wN3fMbMp4fIZ7r7YzJ4B3gJiBI/o\neBsgVdl0tVVEREQkU5mekBxc9xaeTpUspP7LfurD7Kc+zH7qw8ylYE1EREQkg+ndoCIiIiIZ7KAO\n1sxsrJktMbOlZnbDgW6PpGZmD5jZOjN7OyGtm5k9Z2bvhd9dE5Z9O+zTJWZ2xoFptcSZ2RFm9oKZ\nLTKzd8zsq2G6+jBLmFmBmb1uZm+Gffj9MF19mEXMLGpm/zKzP4Xz6r8scdAGawmvtDoTGABMNLMB\nB7ZV0oCHgLFJaTcAz7v70cDz4TxhH04Ajg3L/DLsazlwaoD/dvcBwInA1WE/qQ+zx07gs+4+BBgK\njDWzE1EfZpuvEjxxIU79lyUO2mANvdIqa7j7i8DHScnjgIfD6YeBLySkz3L3ne7+PrCUoK/lAHH3\nte7+Rji9jeBg0RP1YdbwQGU4mxt+HPVh1jCzXsDngPsSktV/WeJgDtb0Sqvs1sPd14bTHwI9wmn1\nawYzsz7AccBrqA+zSngKbQGwDnjO3dWH2eV24JsEj8mKU/9liYM5WJN2woNbmnVbc4Yzs47AE8B1\n7r41cZn6MPO5e627DyV4o8wIMxuYtFx9mKHM7PPAOnef31Ae9V9mO5iDtea8Dksy10dm9kmA8Htd\nmK5+zUBmlksQqJW7++/DZPVhFnL3zcALBNcyqQ+zwynA2Wa2nOCSn8+a2a9R/2WNgzlY0yutstts\n4JJw+hKCV5fF0yeYWb6Z9QWOBl4/AO2TkJkZcD+w2N1vTVikPswSZvYJMysOpwuB04B3UR9mBXf/\ntrv3cvc+BMe6v7n7F1H/ZY10vsg9ozX0OqwD3CxJwcweA0qBQ8xsNfA9YDrwuJl9GVgBXAgQvtLs\ncWARwV2IV7t77QFpuMSdAnwJWBhe8wRwI+rDbPJJ4OHwjsAI8Li7/8nM/on6MJvpbzBL6A0GIiIi\nIhnsYD4NKiIiIpLxFKyJiIiIZDAFayIiIiIZTMGaiIiISAZTsCYiIiKSwRSsiYiIiGQwBWsictAy\ns6Fm9l8J82eb2Q1tVPd1ZtahLeoSkYObnrMmIgctM7sUKHH3qWmoe3lY94YWlInq4aMikkwjayKS\n8cysj5ktNrN7zewdM/tL+NqjVHmPMrNnzGy+mb1kZv3C9AvM7G0ze9PMXgxfM/cDYLyZLTCz8WZ2\nqZn9Isz/kJndbWavmtkyMys1swfCdjyUsL67zWxe2K7vh2nXAocDL5jZC2HaRDNbGLbhpwnlK83s\nf83sTeAkM5tuZovM7C0z+1l69qiIZBONrIlIxjOzPsBSgpGqBeGrcGa7+69T5H0emOLu75nZCcBP\n3P2zZrYQGOvua8ys2N03J4+sJc6HAVkBMBE4G/gVwauz3iF4t/CXw7Z0c/ePw1cxPQ9c6+5vJY6s\nmdnhwKvAcGAT8Bfg5+7+BzNzYLy7P25m3YFXgH7u7vF2tvkOFZGsopE1EckW77t7/N2i84E+yRnM\nrCNwMvDb8D2k9xC81xLgH8BDZnYFwfuAm+OPHvyPdiHwkbsvdPcYQcAWX/+FZvYG8C/gWGBAinqO\nByrcfb271wDlwGfCZbXAE+H0FqAauN/MzgWqmtlOEWnHDtoXuYtI1tmZMF0LpDoNGgE2u/vQ5AXu\nPiUcafscMN/MhrdgnbGk9ceAHDPrC3wdON7dNyWMxrVEdfw6NXevMbMRwBjgfGAq8NkW1ici7YxG\n1kSk3XD3rcD7ZnYBgAWGhNNHuftr7v5dYD1wBLAN6LQPq+wMbAe2mFkP4MyEZYl1vw6MMrNDwtOl\nE4G/J1cWjgx2cfc5wPXAkH1om4i0ExpZE5H2ZhJwt5l9B8gFZgFvAreY2dGAEVxb9iawErghPGX6\nk5auyN3fNLN/Ae8CqwhOtcbNBJ4xsw/cfXT4SJAXwvX/2d2fSlFlJ+ApMysI832tpW0SkfZHNxiI\niIiIZDCdBhURERHJYDoNKiJZyczuIniURqI73P3BA9EeEZF00WlQERERkQym06AiIiIiGUzBmoiI\niEgGU7AmIiIiksEUrImIiIhkMAVrIiIiIhns/wPvGqnL2OxtfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe241cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "from sklearn.model_selection import learning_curve, validation_curve\n",
    "cv = ShuffleSplit(n_splits = 10, test_size = 0.2, random_state = 0)\n",
    "\n",
    "# Vary the max_depth parameter from 1 to 10\n",
    "n_estimators = np.arange(10, 500, 50)\n",
    "\n",
    "# Calculate the training and testing scores\n",
    "train_scores, test_scores = validation_curve(AdaBoostClassifier(), X_train, y_train, \\\n",
    "    param_name = \"n_estimators\", param_range = n_estimators, cv = cv, scoring = 'accuracy')\n",
    "\n",
    "# Find the mean and standard deviation for smoothing\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot the validation curve\n",
    "pl.figure(figsize=(10, 3))\n",
    "pl.title('AdaBoostClassifier Complexity Performance')\n",
    "pl.plot(n_estimators, train_mean, 'o-', color = 'r', label = 'Training Score')\n",
    "pl.plot(n_estimators, test_mean, 'o-', color = 'g', label = 'Validation Score')\n",
    "pl.fill_between(n_estimators, train_mean - train_std, \\\n",
    "    train_mean + train_std, alpha = 0.15, color = 'r')\n",
    "pl.fill_between(n_estimators, test_mean - test_std, \\\n",
    "    test_mean + test_std, alpha = 0.15, color = 'g')\n",
    "pl.ylim([0.6, 1.0])\n",
    "\n",
    "# Visual aesthetics\n",
    "pl.legend(loc = 'lower right')\n",
    "pl.xlabel('n_estimators')\n",
    "pl.ylabel('Score')\n",
    "pl.grid(True)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看到，AdaBoost的性能会随着n_estimators的增长而缓慢上升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 500, 'base_estimator': None, 'random_state': 0, 'learning_rate': 1.0, 'algorithm': 'SAMME.R'}\n",
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.8543\n",
      "F-score on testing data: 0.7178\n",
      "Overall time spend: 1.15000009537\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.8657\n",
      "Final F-score on the testing data: 0.7415\n",
      "Overall time spend: 65.3120000362\n"
     ]
    }
   ],
   "source": [
    "# TODO：导入'GridSearchCV', 'make_scorer'和其他一些需要的库\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# TODO：初始化分类器\n",
    "clf = AdaBoostClassifier(random_state=0)\n",
    "\n",
    "# TODO：创建你希望调节的参数列表\n",
    "parameters = {'n_estimators' : np.arange(100, 600, 100)}\n",
    "# cv_sets = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\n",
    "\n",
    "# TODO：创建一个fbeta_score打分对象\n",
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "start = time()\n",
    "# TODO：在分类器上使用网格搜索，使用'scorer'作为评价函数\n",
    "grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer)\n",
    "\n",
    "# TODO：用训练数据拟合网格搜索对象并找到最佳参数\n",
    "grid_obj.fit(X_train, y_train)\n",
    "end = time()\n",
    "opt_time = end - start\n",
    "# 得到estimator\n",
    "best_clf = grid_obj.best_estimator_\n",
    "print best_clf.get_params()\n",
    "\n",
    "# 使用没有调优的模型做预测\n",
    "start = time()\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "end = time()\n",
    "uno_time = end - start\n",
    "\n",
    "# 汇报调参前和调参后的分数\n",
    "print \"Unoptimized model\\n------\"\n",
    "print \"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5))\n",
    "print \"Overall time spend:\", uno_time\n",
    "print \"\\nOptimized Model\\n------\"\n",
    "print \"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "print \"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))\n",
    "print \"Overall time spend:\", opt_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5 - 最终模型评估\n",
    "\n",
    "_你的最优模型在测试数据上的准确率和F-score是多少？这些分数比没有优化的模型好还是差？你优化的结果相比于你在**问题 1**中得到的朴素预测器怎么样？_  \n",
    "**注意：**请在下面的表格中填写你的结果，然后在答案框中提供讨论。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结果:\n",
    "\n",
    "|     评价指标     | 基准预测器 | 未优化的模型 | 优化的模型 |\n",
    "| :------------: | :--------: | :---------: | :-------------: | \n",
    "| 准确率          | 0.2438     |  0.8543     |  0.8657       |\n",
    "| F-score        | 0.2872     |  0.7178     |  0.7415       |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：** 可以看到：\n",
    "\n",
    "- 朴素预测器（基准预测器）的性能最差。\n",
    "- 未优化的AdaBoost模型就已经远远好过朴素预测期，达到了85%的准确率和71%的F-Score。\n",
    "- 进一步优化的AdaBoost模型由于增加了弱学习器的个数（500个），因此整体性能相比未优化的模型又有的一定程度的进步，准确度提升了1%，F-Score提升了3%。不过相应的，也付出了60倍的运行时间代价。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## 特征的重要性\n",
    "\n",
    "在数据上（比如我们这里使用的人口普查的数据）使用监督学习算法的一个重要的任务是决定哪些特征能够提供最强的预测能力。通过专注于一些少量的有效特征和标签之间的关系，我们能够更加简单地理解这些现象，这在很多情况下都是十分有用的。在这个项目的情境下这表示我们希望选择一小部分特征，这些特征能够在预测被调查者是否年收入大于\\$50,000这个问题上有很强的预测能力。\n",
    "\n",
    "选择一个有`feature_importance_`属性（这是一个根据这个选择的分类器来对特征的重要性进行排序的函数）的scikit学习分类器（例如，AdaBoost，随机森林）。在下一个Python代码单元中用这个分类器拟合训练集数据并使用这个属性来决定这个人口普查数据中最重要的5个特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6 - 观察特征相关性\n",
    "\n",
    "当**探索数据**的时候，它显示在这个人口普查数据集中每一条记录我们有十三个可用的特征。             \n",
    "_在这十三个记录中，你认为哪五个特征对于预测是最重要的，你会怎样对他们排序？理由是什么？_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：** 首先通过领域知识进行推断，然后通过直方图呈现单个特征与收入的分布关系。\n",
    "\n",
    "### 按Domain Knowledge分析\n",
    "\n",
    "1. **年龄**显然是与收入密切相关的。在10-20岁的教育阶段，很少有人能够有超过50K的收入，随着年龄增长，人们步入社会开始工作，工作阅历和经验的增长，收入不断增加是正常的趋势，然后在60岁左右退休之后，收入肯定会逐渐减少。因此年龄应该是决定收入的强相关特征。\n",
    "\n",
    "2. **教育水平**与收入也有很强的相关性。一个人接受的教育年限越长，通常能说明他的学历越高、智力水平高于平均值，因此收入有更大几率超过50K。\n",
    "\n",
    "3. **职业类型**应该会与收入有一定关系。从事IT行业的应该会比清洁工的收入高。但是问题在于同一个行业不同职位的人有可能收入差别也很大，例如清洁工企业的CEO和小兵的收入肯定是不同的，因此分析起来有些难度。\n",
    "\n",
    "4. **婚姻情况**也是一个决定收入的重要因素。婚姻使得男女可以互相照应，互相帮助，共享一些社会资源，因此会比单身群体更容易得到高收入。\n",
    "\n",
    "5. **性别**也可以决定收入。男女平等是一个美好的愿景，但现实生活中男性比女性薪水高的情况屡见不鲜。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按特征的直方图分析\n",
    "\n",
    "#### 1. 数值型特征的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AsAg+AwcAAAC0QtzKnd4OweOVpHu9HQK8hBU4AAAAALAIGjgAAAAAsAgaOAAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xededb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ISEit7TVjXXHmzLnGxnKrNm1CVFLiWjPqTcjtXlbN7U0au36sufv92PWmAQQA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\ngQMAAAAAi/h/l/9W+hFTMowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebeb278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ERER8TKP22F100UWMGDGCwYMH85///IdRo0ZhjMFisQAQFBRERUUFTqcTu/3n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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe47ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H0tTUxPDhw7n55pv59a9/TWFhISNGjKChoYHExESmTJlyxtsNDw+nuLiYefPmsXjxYhob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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb14630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8ObFc27ZtiY+P5+2336Zr164cPHjwouaifFl7dAaujrPZbNxzzz3A2S9KVyU+Pp6kpCSG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\nnJtASOii4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5c40b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "num_features = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "for f in num_features:\n",
    "    dist = data[f][data['income'] == '>50K']\n",
    "    dist2 = data[f][data['income'] == '<=50K']\n",
    "    fig = plt.figure(figsize=(15,2))\n",
    "    plt.subplot(121)\n",
    "    plt.title(('<' + f + '> distribution for Income > 50K'))\n",
    "    plt.hist(dist)\n",
    "    plt.grid(True)\n",
    "    plt.subplot(122)\n",
    "    plt.title(('<' + f + '> distribution for Income <= 50K'))\n",
    "    plt.hist(dist2)\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 标签型特征的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hwoXYsWMHcnJyMGDAAHh5een+INFXWd/fP4WGhiIwMBDh4eHQaDQICQmBg4MD1qxZg+Dg\nYKxevRparRaTJ0/GSy+9hOPHj5fofps2bYqFCxdi1qxZuj2Fa9eufWwPsbOzM5ydnUt0nwEBAQgI\nCEC/fv2Ql5eH4cOHo3v37gCATz75BAsXLsSDBw9ga2uLpUuXFnkfBSfJjx49Gq1bt9brKmNEJAc7\n8SF2YuXvxH+qWrUqBg4cqPfjYF8aLo3gMVAiIiIiIiKlcSkmERERERGR4jjYERERERERKY6DHRER\nERERkeI42BERERERESmOgx0REREREZHilHm5g5SUDNkRnomdnSXS0u7LjlEqKmcHmF8mlbMDaudX\nOTsA1KljLTuCUlTuSNW/V5lfHpWzA2rnVzk7oHb+p/Ujj9hVEDMzU9kRSk3l7ADzy6RydkDt/Cpn\nJ+Oi+vcq88ujcnZA7fwqZwfUz/8kHOyIiIiIiIgUx8GOiIiIiIhIcRzsiIiIiIiIFKfMxVOIiIiI\nqGjjlhyUHaFY6+f2lB2BqFLjETsiIiIiIiLFcbAjIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwH\nOyIiIiIiIsVxsCMiIiIiIlIcBzsiIiIiIiLFcbAjIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwH\nOyIiIiIiIsVxsCMiIiIiIlIcBzsiIiIiIiLFcbAjIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwH\nOyIiIiIiIsVxsCMiIiIiIlIcBzsiIiIiIiLFcbAjIiIiIiJSnNnTPpibm4sPPvgA169fR05ODt55\n5x00adIEc+fOhUajQdOmTTF//nyYmJhg69atiIiIgJmZGd555x306NEDf//9N9577z3cuXMH1atX\nx9KlS1GzZk2cPHkSISEhMDU1haurK6ZMmVJRj5eIiKhMsCOJiMiQPPWI3Y8//ghbW1ts2rQJ//73\nvxEUFITFixdjxowZ2LRpE4QQOHDgAFJSUhAeHo6IiAisW7cOYWFhyMnJwebNm+Hs7IxNmzbB09MT\na9asAQDMnz8foaGh2Lx5M2JjY3Hu3LkKebBERERlhR1JRESG5KmDnYeHB6ZPnw4AEELA1NQUZ8+e\nRefOnQEAL7/8MqKionDq1Cm0b98eFhYWsLa2RoMGDRAXF4eYmBi4ubnptv3111+RmZmJnJwcNGjQ\nABqNBq6uroiKiirnh0lERFS22JFERGRInroUs3r16gCAzMxMTJs2DTNmzMDSpUuh0Wh0H8/IyEBm\nZiasra0LfV5mZmah2x/d1srKqtC2V69eLTaonZ0lzMxM9X+EBqROHeviNzJQKmcHmF8mlbMDaudX\nObsK2JFlR/XvVdXzV5TyeJ5Uf+5Vzq9ydkD9/EV56mAHAH/99RcmT56MYcOGYcCAAfjoo490H8vK\nyoKNjQ2srKyQlZVV6HZra+tCtz9tWxsbm2KDpqXd1+uBGZo6dayRkpIhO0apqJwdYH6ZVM4OqJ1f\n5eyAOoXLjnx2leF7VeX8FamsnyfVn3uV86ucHVA7/9P68alLMW/fvo1x48bhvffew+DBgwEALVu2\nxPHjxwEAkZGRcHFxQZs2bRATE4Ps7GxkZGQgISEBzs7O6NChAw4fPqzbtmPHjrCysoK5uTmuXLkC\nIQSOHj0KFxeXsnqsREREFYIdSUREhuSpR+w+++wz3Lt3D2vWrNGd1O3v74/g4GCEhYWhUaNG6NOn\nD0xNTTFy5EgMGzYMQgjMnDkTVapUga+vL/z8/ODr6wtzc3OEhoYCAAIDAzF79mxotVq4urqibdu2\n5f9IiYiIyhA7koiIDIlGCCFkhygJVQ+XFlD9kK+q2QHml0nl7IDa+VXODqizFNNQqP5vzfzPbtyS\ng7IjFGv93J5len+G8tyXlsr5Vc4OqJ2/1EsxiYiIiIiIyPBxsCMiIiIiIlIcBzsiIiIiIiLFcbAj\nIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwHOyIiIiIiIsVxsCMiIiIiIlIcBzsiIiIiIiLFcbAj\nIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwHOyIiIiIiIsVxsCMiIiIiIlIcBzsiIiIiIiLFcbAj\nIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwHOyIiIiIiIsVxsCMiIiIiIlIcBzsiIiIiIiLFcbAj\nIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwHOyIiIiIiIsVxsCMiIiIiIlIcBzsiIiIiIiLFcbAj\nIiIiIiJSHAc7IiIiIiIixXGwIyIiIiIiUhwHOyIiIiIiIsVxsCMiIiIiIlJciQa72NhYjBw5EgBw\n+fJl+Pr6YtiwYZg/fz7y8/MBAFu3boWXlxeGDBmCQ4cOAQD+/vtvTJ06FcOGDcP48eORmpoKADh5\n8iS8vb0xdOhQfPLJJ+XxuIiIiCoEO5KIiAxBsYPdl19+iXnz5iE7OxsAsHjxYsyYMQObNm2CEAIH\nDhxASkoKwsPDERERgXXr1iEsLAw5OTnYvHkznJ2dsWnTJnh6emLNmjUAgPnz5yM0NBSbN29GbGws\nzp07V76PkoiIqBywI4mIyFAUO9g1aNAAq1ev1r1/9uxZdO7cGQDw8ssvIyoqCqdOnUL79u1hYWEB\na2trNGjQAHFxcYiJiYGbm5tu219//RWZmZnIyclBgwYNoNFo4OrqiqioqHJ6eEREROWHHUlERIbC\nrLgN+vTpg2vXruneF0JAo9EAAKpXr46MjAxkZmbC2tpat0316tWRmZlZ6PZHt7Wysiq07dWrV4sN\namdnCTMz05I/MgNUp4518RsZKJWzA8wvk8rZAbXzq5xdFezIsqH696rq+StKeTxPqj/3KudXOTug\nfv6iFDvY/ZOJyf8f5MvKyoKNjQ2srKyQlZVV6HZra+tCtz9tWxsbm2K/blrafX2jGpQ6dayRkpIh\nO0apqJwdYH6ZVM4OqJ1f5eyAuoXLjtRfZfheVTl/RSrr50n1517l/CpnB9TO/7R+1PuqmC1btsTx\n48cBAJGRkXBxcUGbNm0QExOD7OxsZGRkICEhAc7OzujQoQMOHz6s27Zjx46wsrKCubk5rly5AiEE\njh49ChcXl1I+NCIiIsPBjiQiIln0PmLn5+eHDz/8EGFhYWjUqBH69OkDU1NTjBw5EsOGDYMQAjNn\nzkSVKlXg6+sLPz8/+Pr6wtzcHKGhoQCAwMBAzJ49G1qtFq6urmjbtm2ZPzAiIqKKxo4kIiJZNEII\nITtESah6uLSA6od8Vc0OML9MKmcH1M6vcnZA3aWYsqj+b838z27ckoOyIxRr/dyeZXp/hvLcl5bK\n+VXODqidv0yXYhIREREREZFh4WBHRERERESkOA52REREREREiuNgR0REREREpDgOdkRERERERIrT\n++UOiGQxxit+ERERERGVBI/YERERERERKY6DHRERERERkeI42BERERERESmOgx0REREREZHiONgR\nEREREREpjoMdERERERGR4jjYERERERERKY6DHRERERERkeI42BERERERESmOgx0REREREZHiONgR\nEREREREpjoMdERERERGR4jjYERERERERKY6DHRERERERkeI42BERERERESmOgx0REREREZHiONgR\nEREREREpzkx2ACIiIiIi2cYtOSg7QrHWz+0pOwIZMB6xIyIiIiIiUhwHOyIiIiIiIsVxKSYRERHR\nU6iwRI+IiEfsiIiIiIiIFMfBjoiIiIiISHFciklEREREpAAVlgXzyp3ycLAjMjIsBSIiIqLKR9pg\nl5+fjwULFuDChQuwsLBAcHAwnJycZMUhqPEHv6Hjc0hEZcGYOnLAuz/IjkAVhB1JVL6kDXb79+9H\nTk4OtmzZgpMnT2LJkiVYu3atrDjljsVFVHKGXv48okjlzdg6koiInp20i6fExMTAzc0NANCuXTuc\nOXNGVhQiIiKDwo4kIiJ9STtil5mZCSsrK937pqamyMvLg5lZ0ZHq1LGuqGjlYlfoG7IjEJGRUf33\npjEzpo5kPxKRDCr/3nwSaUfsrKyskJWVpXs/Pz//iYVFRERkTNiRRESkL2mDXYcOHRAZGQkAOHny\nJJydnWVFISIiMijsSCIi0pdGCCFkfOGCK37Fx8dDCIFFixahcePGMqIQEREZFHYkERHpS9pgR0RE\nRERERGVD2lJMIiIiIiIiKhsc7IiIiIiIiBTHwY6IiIiIiEhxHOzKSWpqquwIz0T1/CTXzZs3AQCn\nT5+WnMS48PkmVajcMSpnJ/nYj3IYy/NtumDBggWyQ1RGw4cPR1RUFGxsbNCgQQPZcfSmcn6tVovt\n27dj//790Gg0sLS0RLVq1WTHKrHY2Fjs27cPbdu2xbvvvgtHR0fY29vLjlViAQEBuHXrFjp06IAv\nv/wSkZGRePnll2XHKpFFixbBwcEBNWvWlB2lVJYsWYL169cjOzsbL7zwAqpUqSI7ElGRVO4YlbMD\n7EiZVO5HQO2ONJZ+5FUxy9Hp06exY8cOnDp1Cu7u7njnnXdkR9KLqvn9/f1hb2+PqKgoTJw4EZs3\nb8aXX34pO1aJDRo0CCtWrECDBg1w9epVzJ07Fxs3bpQdq8QGDx6Mbdu26d4fPny4Mvn37t2LHTt2\nICsrC15eXujbty+qVq0qO5Ze7t69i927d2P//v2oWbMmhgwZgi5dusiORfQYVTsGUDs7O1IelfsR\nUL8jjaEfuRSzHDVt2hTt2rWDra0tTpw4ITuO3lTNf+XKFUyfPh1VqlRBz549kZGRITuSXszNzXV7\ngevXrw8TE/V+TNPS0gAA9+7dg1arlZym5Pr06YPPP/8cYWFhOHLkCFxdXWVH0tvt27dx48YNpKWl\nwc7ODnv37sXs2bNlxyJ6jKodA6idnR0pl6r9CKjfkcbQj2ayA1RW77//PmJjY9GnTx8EBgbC0dFR\ndiS9qJxfq9XqzoHIzMxU7pf+c889h7CwMLRr1w6nTp1SZolJgcmTJ2PQoEGoUaMGMjIyEBAQIDtS\nid24cQPff/899u7di1atWim1FxsAvL29UbVqVXh7e2P69OmwsLAAALz55puSkxEVpnLHqJwdYEfK\npHI/Amp3pLH0I5dilpODBw+iR48e0Gg0sqOUisr5o6OjMW/ePKSkpMDBwQH+/v7o1q2b7Fgllp2d\njc2bNyMpKQlNmjSBj4+P7heQKrRaLdLS0lCrVi2lvocGDRoEb29v9O/fH1ZWVrLj6O3SpUt44YUX\nZMcgKpbKHaNydoAdKZuq/Qio3ZHG0o8c7MpJXFwc/P39kZycjNq1ayMkJAStWrWSHavEVM5/+vRp\nvPjii0hNTYWdnR2io6PRuXNn2bFKTAiB06dPIzs7W3dbp06dJCbSz7Fjx/D1118Xyv/NN99ITKSf\n//73v7h48SJeeOEFuLu7y46jlwMHDmDTpk3Izc2FEALp6enYtWuX7FhEj1G5Y1TODrAjZVK9HwF1\nO9JY+pFLMctJSEgIQkJC0Lx5c5w/fx6BgYGIiIiQHavEVMx/4sQJXLx4EV9//TXGjh0LAMjPz8fG\njRuxe/duyelKburUqUhNTYWDgwOEENBoNMqUFgAsXrwYH3zwAerVqyc7it5CQ0Nx+fJldOjQATt3\n7sSJEycwd+5c2bFKbOXKlVi4cCEiIiLQpUsXREVFyY5EVCQVO6aAqtnZkfKp3I+A2h1pLP3Iwa6c\nCCHQvHlzAECLFi1gZqbWU61ifhsbG9y+fRs5OTlISUkBAGg0Grz33nuSk+nn9u3bSvyR8CQODg5K\nLet5VHR0tO65Hz16NIYMGSI5kX7s7e3Rvn17REREwMvLC99//73sSERFUrFjCqianR0pn8r9CKjd\nkcbSj2r8NlKQqakpDh06BBcXF0RHRyu1/htQM7+zszOcnZ3h7e2NunXr6m7Pzc2VmEp/DRs2RHJy\ncqHHoJJatWohICAALVu21J0/4OPjIzlVyeTl5SE/Px8mJia6PcEqMTc3R3R0NPLy8nDkyBHd1deI\nDI2KHVNA1ezsSPlU7kdA7Y40ln7kOXbl5Pr161i6dCkSExPRuHFjzJkzB88//7zsWCWmcv6IiAh8\n9dVXyMvLgxAC5ubm2Lt3r+xYJda7d29cu3at0AuAHj16VGIi/XzyySeP3TZlyhQJSfS3fv167N27\nF23btsXhWSNkAAAYn0lEQVSpU6fg4eGBMWPGyI5VYsnJyUhMTESdOnWwatUqeHh4oF+/frJjET1G\n5Y5ROTvAjpRJ5X4E1O5IY+lHDnbl5MaNGwCg26NhZmYGOzs7mJubS05WMirnHzBgANatW4e1a9fC\nw8MDGzZswJo1a2THqvRu3ryJevXqISkp6bGPNWzYUEKi0omPj0diYiIaNWoEZ2dn2XH0tn//fiQm\nJqJp06bo0aOH7DhERVK5Y1TODrAjZags/Qio3ZHG0I9cillOJk6ciOTkZDRq1AhJSUmoVq0a8vLy\n8N577+GNN96QHa9YKue3t7eHvb09srKy0KVLlyL3kBmiNWvWYNKkSZg1a9ZjyxtCQ0MlpSq5r776\nCu+//z4CAgJ0+Qv+8FHlql/vv/++7u3Dhw/D3Nwc9erVw/Dhw1GjRg2JyUrG398f9+/fR7t27bBz\n50789ttvhR4TkaFQuWNUzg6wI2WoDP0IqN2RxtKPHOzKiaOjIzZs2ICaNWvi7t27mDdvHoKCgjB+\n/HglfvGrnN/a2hr79++HRqNBREQE0tPTZUcqkZ49ewIAhg4dKjlJ6RT8ggwPDwcAnD17VqlLgAMP\nXx+pfv36cHFxQWxsLE6fPo2aNWvCz88Pn332mex4xYqPj8d3330HQL0T28m4qNwxKmcH2JEyVIZ+\nBNTuSGPpRxPZASqrO3fu6NZ/16hRA7dv34atrS1MTNR4ylXOHxwcjOeeew6zZs3CpUuXMG/ePNmR\nSqTgKmudO3dG586dsW/fPt3bKlq6dKnsCHpLTU3FzJkz4ebmhilTpiA3NxczZsxARkaG7Ggl0qBB\nA1y9ehXAw59hBwcHyYmIiqZyx6icHWBHGgIV+xFQuyONpR95xK6ctGrVCrNmzUK7du1w8uRJtGjR\nAj/99BNq1aolO1qJqJzfz88PQ4YMQYsWLZR5fZWixMfHy47wTFQ8fTczMxMJCQlo3LgxEhISkJWV\nhbS0NNy/f192tBKJjY1F37594eDggOTkZFhYWMDV1RWAOhcXIOOgcseonB1gRxoCFfsRULsjjaUf\nefGUcnTgwAEkJCTA2dkZr776KhITE+Hg4IBq1arJjlYiquY/c+YMduzYgZiYGLi7u2Pw4MFK7pl5\n++23DX5pQ1Hy8vJgZmaGvXv3ok+fPrh37x5sbGxkxyqRU6dOYcGCBbh16xYcHBwQEBCAU6dOoXbt\n2ujTp4/seCVWcDlqIkOmascAamdnR8qjcj8ClaMjK30/CipXQUFBsiM8E5Xzp6enixkzZohWrVrJ\njqK3c+fOid27d4u4uDjZUUrs1q1bIjExUXh7e4ukpCSRmJgoLl68KAYNGiQ7mt7OnDkjO8IzGTly\npOwIRCWicseonF0IdmRFqkz9KITaHVnZ+5FLMcuZyksFADXznzhxAjt27MDp06fh4eEBPz8/2ZH0\nsmLFChw/fhxt2rRBeHg43N3d8dZbb8mOVazY2Fhs2LABSUlJ+PDDDwEAJiYmuqUOKlm6dKlSVyr7\nJ8GFGKQIFTumgKrZ2ZEVrzL1I6B2R1b2fuRgV84sLS1lR3gmKubfsGEDhgwZgpCQkMcuiayCI0eO\nYNu2bTAxMYFWq4WPj4/BlxYAuLu7w93dHYcPH8Yrr7wiO84zUf0Xf8eOHWVHICoRFTumgKrZ2ZEV\nrzL1I6B2R1b2fqzEi0zlyMnJKfTfxx9/rHtbBSrnP336NABgyJAh0Gg0OHbsGI4eParcSbH16tVD\nVlYWgIfr8WvXri05kX7q1q2LQYMGwdXVFZ6enjh37pzsSHobOXKk7AilsnDhQgDAjBkzAABz5syR\nGYfoMSp3jMrZAXakIagM/Qio2ZHG0o88YlfGPDw8oNFodHszCt7WaDQ4cOCA5HTFUzn/r7/+ihdf\nfBE//fTTYx9TabnDrVu30KdPHzRv3hwXL16Eubm57nV7IiIiJKcrXkhICEJCQtC8eXOcP38egYGB\nSuQGgOTkZHz00UdITU1Feno6mjVrhrZt28qOVayNGzdi7dq1SE9Px759+wA83KPapEkTycmIClO5\nY1TODrAjDYHK/Qio2ZHG1o+8KiZVOosXL9a9GKiKrl+//sSPPf/88xWYpHRGjBiBb7/99onvG7IJ\nEyZg7NixWLNmDQIDAzF37lxs3bpVdqwS++yzz/D222/LjkFEBowdKY/K/Qio3ZHG0o88YldODhw4\ngE2bNiE3NxdCCKSnp2PXrl2yY5WYyvkTEhKUu4Two+7cuYM9e/YgOztbd9uCBQvkBdKTqakpDh06\nBBcXF0RHR8PCwkJ2pBL7+++/0bVrV6xduxaNGjVClSpVZEfSS926dbFz585Ct3l6ekpKQ/RkKneM\nytkBdqRMKvcjoHZHGks/crArJytXrsTChQsRERGBLl264NixY7Ij6UXl/AkJCXjppZdgZ2enOzFc\npXMI/Pz8MH78eGVLd9GiRVi6dClCQ0PRuHFjBAUFyY5UYlWqVMGRI0eQn5+PkydPKle6iYmJAB4u\nMzl//jxsbW0rZXGR+lTuGJWzA+xImVTuR0DtjjSWfuRgV07s7e3Rvn17REREwMvLC99//73sSHpR\nOf+hQ4dkR3gmTk5O8PLykh2j1J5//nl8/PHHsmOUSlBQEJYuXYq0tDSsX78egYGBsiPp5d1339W9\nLYTAxIkTJaYhejKVO0bl7AA7UiaV+xFQuyONpR852JUTc3NzREdHIy8vD0eOHEFaWprsSHpRMf/v\nv/+OJUuWoHr16ggODoaTk5PsSKXSp08fzJw5E40bN9bdNmXKFImJ9PPZZ5/h3//+N6pWraq7TZW9\nwUeOHMGKFSt073/zzTcYNWqUxET6efTqfCkpKbh27ZrENERPpmLHFFA1OztSPpX7EVC7I42lH3nx\nlHKSnJyMxMRE1KlTB6tWrYKHhwf69esnO1aJqZjf19cXwcHBSE9Px4YNG5TdKzZ48GD07t270DKT\ngit+qeD111/Hli1bUK1aNdlRSmz37t04ePAgjh8/jpdeegkAkJ+fj/j4eOzZs0dyupLr2bOn7u2q\nVavizTffxKBBgyQmIiqaih1TQNXs7Ej5VOxHoHJ0pLH0I4/YlZNly5YhNDQUALB69WrJafSXnZ2N\nO3fuoGvXrnBycjL4y9kCD/eiFuzBU/E5L2Bra4sJEybIjlFqjo6OhfZGqsDNzQ116tRBeno6fHx8\nAAAmJiaoX7++5GT6OXjwIICHFxews7ODiQlfqpQMk8odqWI/AuxIQ6BiPwKVoyONpR852JWTnJwc\nxMXFoWHDhrqTk1U6yXTOnDmYO3cuAODVV1+Fv78/NmzYIDlVyeXn58uOUGp2dnYICAhAy5Ytdd87\nBb9IVZCbm4sBAwbA2dlZl7/gDzhDVaNGDXTp0gVdunTBrVu3kJeXByEEbty4gbp168qOV2LHjx+H\nv78/rKyscO/ePQQFBaF79+6yYxE9RuWOVL0fAXakLCr2I1A5OtJY+pGDXTm5dOkSJk2apHtflRcw\nfVS7du0AAC4uLkqUQHJyMrZs2QIhhO7tAqr80gegO+/h9u3bkpOUzvjx42VHKLUPPvgAJ0+exIMH\nD/DgwQM0aNBAmdfoAR5erW/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      "text/plain": [
       "<matplotlib.figure.Figure at 0xeddfc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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urhg8eLBBpRUdHQ0ga62vEiVKoGHDhrhw4QIyMjIU2xbBwcGoUaMG7t27h0uX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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf32b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+/v5CCCH8/PyEEP/3GlQRGRkpO8LfMmzYMJGcnCyEECI1NVUMGTJEciL9DRo0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\n6WPp0soAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfd32b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0Op3UGnl5eRg0aBDc3Nzg7e2NJk2aSG3fICAgAO+99x5+//136PV6/PTTT1i6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\nQRAEQRAEQTAJMoATBEEQBEEQBEEwCf8HKEK273Uei5kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfbcdc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OkydPhq2tLYKDgxEYGAghBIYPHw5ra2sEBAQgNDQUAQEBsLS0REREhKKDIiIi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb603c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ccrvdCgoKUrVq1SyszHuc+LxfKyUlRf/4xz90+PBh1axZUy+//LLKlCljdVmA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe7ceda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FAD6DfPRfZCTKwxk7+KTnnntOtWvX1vfff68aNWro+eeft7okVIJZs2bpo48+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\nAACwORo7AAAAALC5/wf5r4VDUMbCYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe9f5a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3bq23jhSkqLeXn5mZGVatWoV58+Zh4cKFMDU1xapVq1C+fHmsWLECc+bMQVpaGjQaDYKD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd0a6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "cat_features = ['workclass', 'education_level', 'marital-status', 'occupation', \n",
    "           'relationship', 'race', 'sex', 'native-country']\n",
    "\n",
    "for f in cat_features:\n",
    "    dist = data[f][data['income'] == '>50K']\n",
    "    dist2 = data[f][data['income'] == '<=50K']\n",
    "    labels = data[f].values\n",
    "    le = LabelEncoder()\n",
    "    le.fit(labels)\n",
    "    xlabel = list(le.classes_)\n",
    "    bin_num = len(xlabel)\n",
    "    out = le.transform(dist)\n",
    "    out2 = le.transform(dist2)\n",
    "    plt.figure(figsize=(15,2))\n",
    "    \n",
    "    sp1 = plt.subplot(121)\n",
    "    sp1.set_xlim([-1, bin_num])\n",
    "    plt.title('<' + f + '> distribution for Income > 50K')\n",
    "    plt.hist(out, bins=bin_num)\n",
    "    ylim1 = plt.ylim()\n",
    "    plt.xticks(range(bin_num), xlabel, size='small', rotation='vertical')\n",
    "    plt.grid(True)\n",
    "    \n",
    "    sp2 = plt.subplot(122)\n",
    "    sp2.set_xlim([-1, bin_num])\n",
    "    plt.title('<' + f + '> distribution for Income <= 50K')\n",
    "    plt.hist(out2, bins=bin_num)\n",
    "    ylim2 = plt.ylim()\n",
    "    xlabel = list(le.classes_)\n",
    "    plt.xticks(range(bin_num), xlabel, size='small', rotation='vertical')\n",
    "    plt.grid(True)\n",
    "    \n",
    "    ylim = max(ylim1, ylim2)\n",
    "    sp1.set_ylim(ylim)\n",
    "    sp2.set_ylim(ylim)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用Seaborn可视化指定特征的具体分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xe84e470>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eoH8AOJdyBbEpU6ZowoQJDtMiIiI0Y8aM81rYhx9+qM6dO2v06NE6cOCABg0a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\nWidOnFDPnj3Vq1evNpdr7ty5U/n5+ZKku+66S9nZ2ZKkt956S48//rg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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3625c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style = 'darkgrid', color_codes = True)\n",
    "sns.factorplot(\"income\", col = \"marital-status\", data = data, kind = \"count\", col_wrap = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习 - 提取特征重要性\n",
    "\n",
    "选择一个`scikit-learn`中有`feature_importance_`属性的监督学习分类器，这个属性是一个在做预测的时候根据所选择的算法来对特征重要性进行排序的功能。\n",
    "\n",
    "在下面的代码单元中，你将要实现以下功能：\n",
    " - 如果这个模型和你前面使用的三个模型不一样的话从sklearn中导入一个监督学习模型。\n",
    " - 在整个训练集上训练一个监督学习模型。\n",
    " - 使用模型中的`'.feature_importances_'`提取特征的重要性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HfpL77ruH5577L0sttQwLL7wIl156IWuv/Ql+8pNzOfTQIznllBlrQk855USO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ec14e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：导入一个有'feature_importances_'的监督学习模型\n",
    "\n",
    "# TODO：在训练集上训练一个监督学习模型\n",
    "model = AdaBoostClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# TODO： 提取特征重要性\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# 绘图\n",
    "vs.feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看指定特征的重要性占比\n",
    "\n",
    "可以看到那些被拆散的特征（例如Occupation），虽然没有单独上榜，但是如果将它们合并在一起，其整体的重要性其实是很大的（0.18）\n",
    "（注意：这些特征在使用OneHotEncoder打散之后，已经是新的特征了，因此这里的求和只是象征性的近似）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "capital-loss importance: 0.2\n",
      "capital-gain importance: 0.08\n",
      "hours-per-week importance: 0.06\n",
      "education-num importance: 0.04\n",
      "occupation importance: 0.18\n",
      "age importance: 0.18\n"
     ]
    }
   ],
   "source": [
    "def get_importance(importances, name):\n",
    "    feature_name = np.where(X_train.columns.str.contains(name))\n",
    "    print name, \"importance:\", np.sum(importances[feature_name])\n",
    "\n",
    "get_importance(importances, 'capital-loss')\n",
    "get_importance(importances, 'capital-gain')\n",
    "get_importance(importances, 'hours-per-week')\n",
    "get_importance(importances, 'education-num')\n",
    "get_importance(importances, 'occupation')\n",
    "get_importance(importances, 'age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7 - 提取特征重要性\n",
    "观察上面创建的展示五个用于预测被调查者年收入是否大于\\$50,000最相关的特征的可视化图像。\n",
    "_这五个特征和你在**问题 6**中讨论的特征比较怎么样？如果说你的答案和这里的相近，那么这个可视化怎样佐证了你的想法？如果你的选择不相近，那么为什么你觉得这些特征更加相关？_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：** \n",
    "- AdaBoost模型返回的5个最重要特征是：capital-loss, age, capital-gain, hours-per-week, education-num.\n",
    "- GradientBoosting模型返回的5个最重要特征则是：capital-gain, capital-loss, marital-status_Married-civ-spouse, age, education-num.\n",
    "\n",
    "可以看到两个算法的交集是capital-loss，capital-gain，age, education-num。基本可以看到模型都一致认为年龄、教育年限、投资收益和损失。其中年龄和教育年限与我的预估想法一致。但是我很意外看到投资收益和损失这两项特征竟然会占到几乎30%重要性的程度。\n",
    "\n",
    "- 首先我查了一下投资收益和损失的定义，指的是区别与工资的一种收入。基本上一个人拥有的所有有形财产（车、房）和无形财产（股票、基金），都属于这个人的资产（Capital Asset），当你的资产卖出的时候比买入的时候还多，那么多出来的钱就算Capital Gain，如果少的话少的钱就是Capital Loss。\n",
    "\n",
    "- 针对投资收益和损失的这两个特征的分布，我画了4个直方图（见下个Cell），分别显示了到底收入大于50K和小于50K的人群中的投资收益和损失情况。可以得出如下结论：\n",
    "    - 绝大多数的富人和穷人的投资收益和损失都是0，非零值的人总数两个特征全加起来才5000人.\n",
    "    - 取得投资收益的人中，富人比穷人多\n",
    "\n",
    "#### 如何理解AdaBoost计算capital-loss和capital-gain这两个特征的重要性总体能够占比20%？\n",
    "\n",
    "我所能看到的是，投资收益 / 损失大于0的人本身就少（2%），在这么少的两三千人中，穷人和富人都有，那么究竟是怎么区分的呢？而且竟然能达到20%这么大的重要性？有没有什么数据可视化的方式（在更高维度上？）能够让我们直观的感受到这种划分的存在呢？\n",
    "\n",
    "经过一些实验，我发现不同的算法（具有`feature_importance_`属性的模型），尽管评估指标基本相近，但是对于特征重要性的排序却有很大变化。因此，**实际上这些排序的意义仅限于对应的模型**。\n",
    "\n",
    "另一方面，可以通过将决策树可视化来进行分析，看看到底训练出来的模型是什么样的。越靠上的就是越重要的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.847097844113\n",
      "F0.5 = 0.706856935542\n",
      "capital-loss importance: 0.0477773896135\n",
      "capital-gain importance: 0.214703801592\n",
      "hours-per-week importance: 0.0194255052107\n",
      "education-num importance: 0.234190886277\n",
      "occupation importance: 0.000310362807244\n",
      "age importance: 0.0118588456374\n",
      "marital-status importance: 0.471378295052\n"
     ]
    },
    {
     "data": {
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PeSjK3f9DOPj9mnDCfStr2FeEO2pPJDxbLDtvowhNUZ9l5eltwsEyX7+yMYSavLomwljr\nuWd9ecyj7nl1ZtbczP5lZr+NeXZ3v5qwTtYrJTEPd5O+SzhB1ol93Vaj4beZkpjZvbEGB3f/xt3v\nI9zQkFmuxJ7b5+6fEdbhkYRmuYeyBo8iBO7f52yTR7Kgmbi+9L8A3iTUSu1LuLM3d5x6y8XM1jSz\n/5rZQXGad2Pz7+c0wnrKMopwLHg7a318Qah525zQtPgVWftotHeRNMcAm1u4ExgACzc2PU24yauU\n5fo74Ua2CwhNrLnluDj78OIoJe3tgWfd/QUPfaFhwfEgc+zMt4wzgLqb+WJN4lbFMlPKcdTMtjCz\nSWa2tbvXuPsYwg1X8yjxWCINSzWAUlDs53Yyi/ZtuonQz+WF2PEXwl17cyntztzFMZ7QZHy5hRtG\nWhKa7LoAtWZWltWcu4jYfHwLoRZxXKyJyxz85ni4QeUK4G9mNp3QAXxNwpVsDfBB7Et3PXCRmc0m\nNEGfRqglKaXj9KuEk+xIj49DyPIi4QaTJ3KaQIYQgsYXzOxaQq3ryYR+XfvlzsDdJ5jZn4Bb4wH7\n8zh9e+q5WSWPacA2ZrYL4QT3D0Lt34+E8uhGqFnK1xxXSD9ghJk9AtxPOOBfQwiCnik2YYJeI9wN\nfA3hzux1CeWaebxNplN6LzP72N3fa+D5Dydsm9MI/fsyniQEb0+b2ZXAfwm1eKez4G7tUvyF0L+0\nmsJBdtFycff58WaLW+J29QWhZrIjCx4zMw2oMLMDCDeUJPEsv98RArZHzWwI4TFHlxPKbFys3byC\n0L/sa0J5/orwWKlCgdwQQsD0lJn1i+P9jtBH+WVCMzwUKf8434djOu+7e3ZryWLtw4uplLTfBPY3\ns2MJ29CuLOgPmVm2TGvBPmb2fbyh5Bmgr5m9Q6iJvoBFbx7Kp+hxlHB+mEHY5/rHPB8bhyfxQHup\nh2oApSh3f4WF+6Zlahd2IlxxP0CoNfmM0LzaoA8mdvfphINaJaGP1O2EJp9DCNvvdvUk0Zvw/Kt9\nCc06YwkntzeIJzAPdzofQHgkyxOEIPYNwvPrMv3lriD0XTmD0JdwOuFxMqV4JebhxTzDXozDspt/\nMw+z3pkQcA0mNKt0BA7wwq+JO43QBHV1/P85oeanpAdLZ7mGcBfkM0AHwolmGOEO1OcIfXfOjTVm\nJfFwB+mBMd0RhMetPATskScobhTuPoxwx2QfQq3PQELQdFocPoPQxHk08ZFBDexRwon1L+5e15cr\nro89CEHMwJi3nQh9Lu/Kl1ABjxG2rccKXSSVWC6HEwKigYTy3wM40uMdyoRt7W3Ctnb0YuSvZO7+\nNiGAqSIs132Ebg893P3LOM59hCCoT1yWtQj7QqE0pxH2sY8Jj465l1Ajup+7z1uM8h9OuEksu/Zv\nSffhkpSY9rmEbehmwkXNboQa0v+w4Ma0D+OyXUx4bAuEYPaVmO7Q+PmBEvJU9Djq7vMINbIfE5ry\nnyLU6u7rjfegdMlSVltbSmAvIsuy2ATfG3gyu1N9vBt0srvnNo2JiEiKqQlYZMUwG7gDOCTeVTeP\nUEvajRL6KoqISLqoCVhkBRAft9ObcLfew4QmmC6E5qyXik0rIiLpoyZgERERkZRRDaCIiIhIyiw3\nfQCrq2empqqysrKCqVMLvhhDlmMq2xWTynXFpHJdMaWtXKuqWud9Zq5qAJdB5eXNmzoLkhCV7YpJ\n5bpiUrmumFSugQJAERERkZRRACgiIiKSMgoARURERFJGAaCIiIhIyigAFBEREUkZBYAiIiIiKZNo\nAGhm25nZq3l+38/M3jSzN8zspCTzICIiIiILS+xB0GZ2AXA08EPO7y2Am4Bt47DRZvaEu3+dVF5E\nZPG1e7pNg6b3zd4zGjQ9ERFZcknWAH4CHJTn902BCe4+1d3nAqOAnRPMh4gsByZN+orevXfhzDNP\nrvu7//57FjudESMeZ968eQ2Sp2effYpBg66r+z5w4NUcc8yhdd+ffvpJbrnlhoLTX3LJ+QWHTZr0\nFSeffNwiv0+ePJlRo/6+ZBkWESlRYjWA7v6YmXXKM6gNMD3r+0xgtfrSq6ysSNXTu6uqWjd1FiQh\naS3b+pZ7zpxV2XjjjXjkkYeWaj7Dhz/A0Ucfxsorr7xU6QDssceuPPron+ryPmGC065dFXPnzqBD\nhw589NF77LPPPkD+5bvnnsEF054zZ1VatGi+yHSvv/4CEydOpE+ffZY6/7L00rq/ruhUrk3zLuAZ\nQPaabw1Mq2+ilL23j+rqmU2dDUlAmsu2vuX+7rsf+Omn+XnHGzz4Nt57bxw1NTUceuiR7LprL8aN\ne5v777+HmpoaZs+eTb9+A3j//XFUV1dzxhlnccghhzNixGNceeW1AOy//x488cRzXH11f6ZPn86M\nGdMZOPBmhg9/cJG0M8rKWjJ/fi2ffPI/qqurWWeddenceRNGjnyOgw46hHHj3uWss85j5syZnHfe\nBUyfHq5tf/vb89lww43q5vnRR//ixhsHUlFRQWVlJSuttDLHH38y1dVTOPHEk5kyZQobbbQx5513\nMXfeOZgff/yRDTfchO7dd2nAEpDFleb9dUWWtnItFOw2RQD4b2BjM2sLfE9o/h3UBPkQkWXMZ599\nyplnnlz3vV+/AUyY8DGTJn3JnXfex5w5czjllL5su+12fPrpRK644nesuWYVDz44hFdeeZFjjz2B\noUPvo3//a/jwww8Kzmebbbpy6KFH8sYbo/Om3br1ggNm167b8v777/H555/SrdsOdO68CYMH30a3\nbjuw1lrtWXnlVRg8eDDbbPML+vQ5mC+++C/XXHMld955X10agwZdy2WXXcUGG2zIXXfdzpQp1QDM\nmvUDF1/cj1atWnHooX2YMWM6Rx11HJ9//pmCPxFJVKMFgGZ2BNDK3e82s3OA5wh9EIe4+5eNlQ8R\nWXZ16rQ+t91290K/Pf/8M7iPrwsM582bx+TJX1FVVcXNN19Py5YVVFd/w89/3qVo2rW1tXWf11uv\nIwATJ07Im3br1lY3bteu2zFu3Fu4j6d//2uorKykuvprxo17m27dtgfgP//5D998M4aXXnoegJkz\nF77hZcqUKWywwYYAdOmyVd147dt3oE2bcLNNZWUlP/7442KsLZGm0+7pxxNN/5u9891CIA0p0QDQ\n3T8DusXPw7N+fxJ4Msl5i8iKoWPHTmy1VVcuvPBSampqGDr0Xjp0+D/OPvtMHn30b1RUrMqAAf3q\nxi8ra0ZtbS0rrbQy3377LQCTJ09ixozpC41TLO1sW265NcOG3Q+EIA1g0003Y+TIEVx00eUAbLDB\nBvTo0Zvevfdk6tTvePLJvy2URrt2a/HppxNZf/0NFqqZLCsrW2R5y8rKqK2tWeL1JSJSiqZoAhaR\n5cCy8tiWHXfcmXHj3ub0009k9uxZ7LxzTyoqVmWPPfbi9NNPomXLVaisXKOuWbVLly0577xfc/PN\nd9CqVStOOulYOnVan/btO5ScdraWLVtSXl5Oly5b1f3WrduOvPnmP+jYsRMAp556KueffyFPPPE4\ns2b9wPHHn7xQGueeeyHXXnsVLVtW0KJFOVVV7Qou74YbbsSDDw6hc+dN6NVrjyVdbSIiRZVlN4ss\ny6qrZy4fGW0AaeugmiYq2xVTfeX62GOPsuuuu1NZWcndd99BixYt6NtXz8Bf1ml/LWx5bgJOW7lW\nVbVetKkB1QCKiCSubdu2nHPOGbRsWUGrVq249NL+TZ0lEUk5BYAiIgnr2bMXPXv2qn9EEZFGogBQ\nREQSszw3FYqsyJJ8FZyIiIiILIMUAIqIiIikjJqARSSvhm66U1OdiMiyQzWAIrLMmDjxE84//zec\nddYpnHjiMdx3310k8aiqq6/uz9ixYwoO/+STCbz77jsA9Ot3MT/99NMSzWfSpK/o3XsXzjzz5Lq/\n+++/Z7HTGTHicebNm7dEecj17LNPMWjQdXXfBw68mmOOObTu+9NPP8ktt9xQcPpLLjm/4LBJk77i\n5JOPW+T38unTWfXjj5cswyKSCNUAisgyYebMmfTvfwlXX3096667HvPnz+fyyy9ixIjHOPDAgxs1\nL6+++hJrrLEGW265NVdeee1SpZXv9XaLa9iw+9lzz30oL1/6Q3bXrtvx0EPD6r6PH/8RlZVtmTx5\nEmuv3Z533nmr6AOor7nm+sWeZ8Xnn7PSt9/yw8YbL1GeRaThKQAUkWXCqFGvsfXW27LuuusB0Lx5\ncy677EpatGjBO++8xYgRj9UFY/vvvwdPPPEcV1/dn/LyciZPnsRPP/3Ebrv1ZvTov/P115O57rob\n+frryXmny/jhh++57roBfP/9TKZMqeagg35F9+4788wzIykvb0HnzptwxRUX8+CDD9O375EMHfoQ\nLVu2ZPjwYTRv3owePXZj4MBrqK2dR1lZORdccAlrrbV2Scs7ePBtvPfeOGpqajj00CPZdddejBv3\nNvfffw81NTXMnj2bfv0G8P774/juu2/p3/8SDjnk8ILrYfr06cyYMZ2BA29m+PAHF0k7Y8011wTK\nmDFjOtXV1ay3Xic6d96EMWNGcdBBh/Dvf3/I+edfzPfff891113F9OnhFXq//e35bLjhRnXz/Oij\nf3HjjQOpqKigsrKSlVZameOPP5lp06Zy8cXnMmXKFDbaaGPYbBPajhlD2bx5zO7QgR86d17qbUVE\nlp6agEVkmTBlSjXrrLPw69oqKipo0aJF0enWXrs9N910Ox07dmLSpC8ZNOhWevTYjdGj/17vPP/3\nv//Rq1dvbrrpdm666XYeeeRPVFW1Y6+99uWww47gZz/bHIDmzcvZZZddefXVlwB48cVn2XPPfbj9\n9ls4+OBDGTZsGIcffhSDB9+2yDw+++zThZqAq6u/4Y03RjNp0pfceed93HrrYB58cAgzZ87k008n\ncsUVv+O22+5ml1168sorL7LvvgfStu0a9O9/TdFl2WabrgwePIQPP/wgb9rZunbdlvfff4+xY0fT\nrdsOdOu2A2PHjuGrr75krbXas/LKq/Dgg0PYZptf8Ic/3MUFF1zKoEEL14QOGnQtl1zSj1tvHcw6\n6yx4f/KsWT9w8cX9uOuu+3nrrTdpPns23+2wAzM320zBn8gyRDWAIrJMWGut9vznP+MX+u2rr77k\nm2++XmTc7H6BnTtvAkCrVq3r3s3bunVr5syZW3Q6CG/oePTR4bz22itUVKxatJ/dfvsdyKBB19Gx\nYyfWXbcjq622OhMnTmDYsPv585//xNy582jefNFDar4m4Oeffwb38Zx5Znhn8Lx585g8+Suqqqq4\n+ebradmygurqb/j5z7sUzE/u8qy3XkcAJk6ckDft1q2tbtyuXbdj3Li3cB9P//7XUFlZSXX114wb\n9zbdum1fl84777zFSy89D8DMmQu/G3rKlClssMGGAHTpslXdeO3bd6BNmzYAVFZWUraE/SdFJFkK\nAEVkmbDjjt0ZNmwIffocTIcO/8e8efP4wx9uYtttt6Nz50349ttvAZg8eRIzZkyvm66sLO9rLgFY\naaWVC04H8PDDf2TzzbegT5+Deeedt3jjjVEANGvWjJqahYPF0DRdy/Dhw+jTJ/RJXG+9Thx++FHs\numt33nrrA8aNe7ukZe3YsRNbbdWVCy+8lJqaGoYOvZcOHf6Ps88+k0cf/RsVFasyYEC/rGVsRm1t\nbdHlKStrVjTtbFtuuTXDht0PhCANYNNNN2PkyBFcdNHlden07v0zevfek6lTv+PJJ/+2UBrt2q3F\np59OZP31N+DDDz/Iysei5VFbVgbLyXvnRdJCAaCI5NXYj21ZddVWXHrplfz+9wOoqalh1qxZ7Ljj\nTvTpczDz58+nVatWnHTSsXTqtD7t23eoP0Fgk002LTrdjjvuzE03DeSll56nVatWNG/enLlz52K2\nKXfccQudOq2/0Pj77HMA9903mK237grAGWf8hhtuuI4hQwbz/fc/8JvfnFdSvnbccWfGjXub008/\nkdmzZ7Hzzj2pqFiVPfbYi9NPP4mWLVehsnINpkypBqBLly0577xfc/PNd9S7Hgqlna1ly5aUl5fT\npctWdb9167Yjb775j7pa1GOOOZ7rrvsdTzzxOLNm/cDxx5+8UBrnnnsh1157FS1bVtCiRTlVVe0K\nLu/cqirntQDoAAAdcElEQVTWGD2aOWutxczNNitpHYlIssqSeMRCEqqrZy4fGW0AVVWtqa6eWf+I\nstxR2a6Y0liujz32KLvuujuVlZXcffcdtGjRgr59T1pkvOX5VXBpLNdSqVyXH1VVrfM2k6gGUERE\nFlvbtm0555wzaNmyglatWnHppf2bOksishgUAIrIMiHJGgW9haTh9ezZi549e9U/oogsk/QYGBER\nEZGUUQAoIiIikjIKAEVERERSRgGgiIiISMooABQRERFJGQWAIiIiIimjAFBEREQkZRQAioiIiKSM\nAkARERGRlFEAKCIiIpIyCgBFREREUkYBoIiIiEjKKAAUERERSRkFgCIiIiIpowBQREREJGUUAIqI\niIikjAJAERERkZRRACgiIiKSMgoARURERFJGAaCIiIhIyigAFBEREUkZBYAiIiIiKaMAUERERCRl\nFACKiIiIpIwCQBEREZGUUQAoIiIikjIKAEVERERSRgGgiIiISMooABQRERFJGQWAIiIiIilTnlTC\nZtYMuAPoAswBTnT3CVnDjwTOBeYDQ9z9zqTyIiIiIiILJFkDeCCwirtvD1wE3JAzfBDQC9gRONfM\nKhPMi4iIiIhESQaA3YFnAdx9LNA1Z/j7wGrAKkAZUJtgXkREREQkSqwJGGgDTM/6Pt/Myt19Xvz+\nL+Bt4AfgcXefViyxysoKysubJ5PTZVBVVeumzoIkRGXb+Bpjnatcm0bS613l2jRUrslLMgCcAWSv\n4WaZ4M/MtgD2AdYHvgf+aGaHuPufCyU2deqsBLO6bKmqak119cymzoYkQGXbNJJe5yrXppPkele5\nNh2Va8MpFOwm2QQ8GtgbwMy6AR9kDZsOzAZmu/t84BtAfQBFREREGkGSNYB/BXY3szGEPn59zewI\noJW7321mdwGjzGwu8AkwNMG8iIiIiEiUWADo7jXAqTk/j88aPhgYnNT8RURERCQ/PQhaREREJGUU\nAIqIiIikjAJAERERkZRRACgiIiKSMgoARURERFJGAaCIiIhIyigAFBEREUkZBYAiIiIiKaMAUERE\nRCRlFACKiIiIpIwCQBEREZGUUQAoIiIikjIKAEVERERSRgGgiIiISMooABQRERFJGQWAIiIiIimj\nAFBEREQkZRQAioiIiKSMAkARERGRlFEAKCIiIpIyCgBFREREUqa8qTMgsjjaPf14oul/s/dBiaYv\nIiKyLFANoIiIiEjKKAAUERERSRkFgCIiIiIpowBQREREJGUUAIqIiIikjAJAERERkZRRACgiIiKS\nMgoARURERFJGAaCIiIhIyigAFBEREUkZBYAiIiIiKaMAUERERCRlFACKiIiIpIwCQBEREZGUUQAo\nIiIikjIKAEVERERSRgGgiIiISMooABQRERFJGQWAIiIiIimjAFBEREQkZRQAioiIiKSMAkARERGR\nlFEAKCIiIpIyCgBFREREUkYBoIiIiEjKKAAUERERSZnypBI2s2bAHUAXYA5wortPyBq+LXAjUAZM\nBo5y9x+Tyo+IiIiIBEnWAB4IrOLu2wMXATdkBphZGXAP0NfduwPPAh0TzIuIiIiIREkGgJnADncf\nC3TNGtYZ+BY428xeA9q6uyeYFxERERGJEmsCBtoA07O+zzezcnefB6wJ7ACcCUwARprZW+7+cqHE\nKisrKC9vnmB2ly1VVa2bOgup1BjrXWXb+FSuK66k17vKtWmoXJOXZAA4A8hew81i8Aeh9m+Cu/8b\nwMyeJdQQFgwAp06dlVQ+lzlVVa2prp7Z1NlIpaTXu8q2aahcV1xJrneVa9NRuTacQsFukk3Ao4G9\nAcysG/BB1rCJQCsz2yh+3wn4MMG8iIiIiEiUZA3gX4HdzWwM4U7fvmZ2BNDK3e82sxOA4fGGkDHu\n/lSCeRERERGRKLEA0N1rgFNzfh6fNfxl4BdJzV9ERERE8tODoEVERERSpt4A0Mw65PntZ8lkR0RE\nRESSVrAJ2Mzaxo9Pm1kPQj8+gBbACGDjZLMmIiIiIkko1gfwIWD3+PnbrN/nEW7wEBEREZHlUMEA\n0N33ADCzIe5+fONlSURERESSVO9dwO5+fOwH2I4FzcC4+ztJZkxEREREklFvAGhmvwPOA74GauPP\ntcAGCeZLRERERBJSynMAjwY6ufvXSWdGRERERJJXynMAqxX8iYiIiKw4ij0GZuv4cZyZ3QIMB37K\nDFcfQBEREZHlU7Em4Mdyvu+f9Vl9AEVERESWU8UeA7N+Y2ZERERERBpHKXcBD8n5qRaYBfwLuNfd\n5yeRMRERERFJRik3gZQBWwMfAO8CmwHrAXsANyeXNRERERFJQimPgdkU2MndZwKY2b3A88BOhFpA\nEREREVmOlFIDWJkJ/qLZwGruXgvMTSZbIiIiIpKUUmoAx5rZH4H7CM3BfYF/mNlewA9JZk5ERERE\nGl4pNYCnAl8ANwEDgYnAmUAb4JTksiYiIiIiSai3BtDdZwMXx79sjySSIxERERFJVLE3gYxy9+5m\nNpPw6JeMMqDW3dsknjsRERERaXDFagAPif83b4yMiIiIiEjjKNgH0N0nxf+fA9sCJwHVwA7xNxER\nERFZDtV7E4iZXQScBvwKaAn0M7PLk86YiIiIiCSjlLuADwP2Bn5w92+BbsARieZKRERERBJTSgD4\nk7vPyXxx92nAT8llSURERESSVMqDoL8ws32AWjNbGTgPUB9AERERkeVUwRpAM2sdP54JnANsQXjz\nx17AGclnTURERESSUKwGcIqZjQKeAk4nvA2kec57gUVERERkOVMsAPw/YFdgN0KNXy3wlJk9Bbzq\n7nMbIX8iIiIi0sAKBoDuXk143dsjAGbWEegF/B7YCGhdaFoRERERWXbVexOImXUCDgB6A1sB7wB3\nJ5stEREREUlKsXcBXw3sT6jpewa4A3jZ3Wc3Ut6WSrunH094DsclnP6y45u9ZzR1FkRERKQBFasB\nvBh4ArjO3cc2Un5EREREJGHFAkAD9gOuNbPOwAvASOA53QksIiIisvwq+BxAd//Y3W90957AZsBz\nQB/gIzN7obEyKCIiIiINq5RXwQGsB1QBqwBzgXmJ5UhEREREElXsJpBfAz2AXYBvCTeC3Au84u4/\nNkruRERERKTBFesDuCch6LvA3Sc0Un5EREREJGHFHgS9d2NmREREREQaR6l9AEVERERkBaEAUERE\nRCRlFACKiIiIpIwCQBEREZGUUQAoIiIikjIKAEVERERSRgGgiIiISMooABQRERFJGQWAIiIiIilT\n7FVwS8XMmgF3AF2AOcCJ+V4pZ2Z3A9+5+0VJ5UVEREREFkiyBvBAYBV33x64CLghdwQzOwX4eYJ5\nEBEREZEcSQaA3YFnAdx9LNA1e6CZ7QBsB9yVYB5EREREJEdiTcBAG2B61vf5Zlbu7vPMrD3QD+gD\n/KqUxCorKygvb55ANqU+VVWtmzoLjaYxljVN63NZoXJdcSW93lWuTUPlmrwkA8AZQPYabubu8+Ln\nQ4A1gaeBtYEKMxvv7kMLJTZ16qyk8in1qK6e2dRZaDRJL2tVVetUrc9lhcp1xZXkele5Nh2Va8Mp\nFOwmGQCOBvYDHjWzbsAHmQHufitwK4CZHQdsUiz4ExEREZGGk2QA+FdgdzMbA5QBfc3sCKCVu9+d\n4HxFREREpIjEAkB3rwFOzfl5fJ7xhiaVBxERERFZlB4ELSIiIpIyCgBFREREUkYBoIiIiEjKKAAU\nERERSRkFgCIiIiIpowBQREREJGUUAIqIiIikjAJAERERkZRRACgiIiKSMgoARURERFJGAaCIiIhI\nyigAFBEREUkZBYAiIiIiKaMAUERERCRlFACKiIiIpIwCQBEREZGUUQAoIiIikjIKAEVERERSRgGg\niIiISMooABQRERFJGQWAIiIiIimjAFBEREQkZRQAioiIiKSMAkARERGRlFEAKCIiIpIyCgBFRERE\nUkYBoIiIiEjKKAAUERERSRkFgCIiIiIpowBQREREJGUUAIqIiIikjAJAERERkZRRACgiIiKSMgoA\nRURERFJGAaCIiIhIyigAFBEREUkZBYAiIiIiKaMAUERERCRlFACKiIiIpIwCQBEREZGUUQAoIiIi\nkjLlTZ0BkWVJu6fbNHUWGs03e89o6iyIiEgTUQ2giIiISMooABQRERFJGQWAIiIiIimjAFBEREQk\nZRQAioiIiKSM7gIWkRWe7u4WEVlYYgGgmTUD7gC6AHOAE919Qtbww4HfAvOAD4DT3b0mqfyIiIiI\nSJBkE/CBwCruvj1wEXBDZoCZtQQGAD3dfUdgNWDfBPMiIiIiIlGSAWB34FkAdx8LdM0aNgfYwd1n\nxe/lwI8J5kVEREREoiT7ALYBpmd9n29m5e4+Lzb1fg1gZmcBrYAXiiVWWVlBeXnzxDIrhVVVtW7q\nLEgCVK4rprSVa9LLm7b1uaxQuSYvyQBwBpC9hpu5+7zMl9hHcCDQGfilu9cWS2zq1FnFBkuCqqtn\nNnUWJAEq1xVT2so1yeWtqmqduvW5rFC5NpxCwW6STcCjgb0BzKwb4UaPbHcBqwAHZjUFi4iIiEjC\nkqwB/Cuwu5mNAcqAvmZ2BKG59y3gBOB14GUzA7jF3f+aYH5EREREhAQDwNjP79Scn8dnfdZDqEVE\nRESagIIwERERkZRRACgiIiKSMgoARURERFJGAaCIiIhIyigAFBEREUkZBYAiIiIiKaMAUERERCRl\nFACKiIiIpIwCQBEREZGUUQAoIiIikjIKAEVERERSRgGgiIiISMooABQRERFJGQWAIiIiIimjAFBE\nREQkZRQAioiIiKSMAkARERGRlFEAKCIiIpIyCgBFREREUkYBoIiIiEjKKAAUERERSRkFgCIiIiIp\nowBQREREJGUUAIqIiIikjAJAERERkZRRACgiIiKSMgoARURERFJGAaCIiIhIyigAFBEREUkZBYAi\nIiIiKaMAUERERCRlFACKiIiIpIwCQBEREZGUUQAoIiIikjIKAEVERERSRgGgiIiISMooABQRERFJ\nmfKmzoCIiMiSavd0m6bOQqP5Zu8ZTZ0FWYGoBlBEREQkZRQAioiIiKSMAkARERGRlFEAKCIiIpIy\nCgBFREREUkYBoIiIiEjKKAAUERERSRk9B1BERESWKXq+Y/JUAygiIiKSMgoARURERFImsSZgM2sG\n3AF0AeYAJ7r7hKzh+wFXAPOAIe5+T1J5EREREZEFkqwBPBBYxd23By4CbsgMMLMWwE1Ab2AX4GQz\nWyvBvIiIiIhIlGQA2B14FsDdxwJds4ZtCkxw96nuPhcYBeycYF5EREREJCqrra1NJGEzuxd4zN2f\nid//C2zg7vPMrDtwlrsfGoddBfzX3e9NJDMiIiIiUifJGsAZQOvsebn7vALDWgPTEsyLiIiIiERJ\nBoCjgb0BzKwb8EHWsH8DG5tZWzNbidD8+0aCeRERERGRKMkm4MxdwFsAZUBfYGuglbvfnXUXcDPC\nXcC3J5IREREREVlIYgGgiIiIiCyb9CBoERERkZRRACgiIiKSMgoARURERFJmuQkAzezx+P/nZlbw\nodFm1sPMHi4xzbZmdkQ94/Qxs3UWL7clzfszM3s257dzzGyJOmWa2ZZmdkWJ425iZq+WOO5xZrb/\nkuRpeWNm15nZcQ2UVh8zW8fM1jazOxoiTWlc2ftUfccBM+tvZqcu5fwuMrNfLE0aUpqGKNt4DF8l\nyXyuaOL55LqmzkdTMbNXzWyTps5HRmLvAm5o7n5Q/PhLYDLw9wZIdgtgf2B4kXF+A5wKfNUA88vV\nwczWdPcp8fvewNQlScjd3wXebbCcLUh3aEOnmRK/AU519/HA6U2dGVl8OftUkseBzPxSe2JsbI1d\ntiLLosQDwFijsh/QEmgP3AIcAGwOnOfuI8zsTOAgYFVgCtAHOAI4nlBL2Q/4E7ANcBww18zeAdYD\nzgBaALVxukL5OAi4EPiJsKMfBlwKdDGzk4ExwI1Ac2BN4DSgEtgSeNDMjgIedPduMb2xMY0OhPcc\n/wTMAg5295klrp4/A4cAd5rZpsAncb1gZpvn5sfdx5jZ58B44KOYvzXi3/XAoe5+mJkdApwDzAdG\nuftFZtY+rsMyQgCdbx1VAQ8Aq8fxjgGOjON3Bt5z9wfMbG3gKXffJmf6q4GehO3qMXf/faxpHA9s\nEtM81N0nm9kNhNcFAgx391vMbCjwsLs/a2Z7Aoe5+3Fmdj+wEWEbusXdh5nZLsDVcRk/AU5x95+K\nrez4DurBwMaE7eqyuO4uA6qBlYDxZtaDELwdFqeb7O5rm9nGwL1xvFmE8l8rt5wosN2Y2e7AAOBH\n4FvC9r0lYbucC2wQl//qnHz3yDdOkfU1gbA9dwZeAlYDfgG4ux9dbB2taMysJXA/0JFQbucQjhmr\nA+sAt7v7nfm20/j5VGAYC8qzO3Al4dWWaxD2ib5F5r8vcBUwnXBx9z7wO+AuYF3CMfEJd78sU57A\n2oSLwQpgQ+D3uhBbVFOXbVY+OgFDCMe9WuDX7v5egePWIsfIBlgVy5tuZvY8UAXcCXxK/uNivmPw\nUBac8w4AHiEcy1eJ4y9UCVLk/HMtsBPhuH2ju/85jvsN0BbYw93nxzR+A7Rw90FmNhiY6+6/NrNL\nY94/AG6N6X8LHO/u0/PNIytf+xG21z7u3mQvwWisJuDW7r438HvCCfIg4GSgb3xe4BpAL3ffjrBj\nbBunm+ru3d39JQB3/xIYSliZ/ySc4PZx9+6EgGiPInk4HLg+jjsSaEMIIF5297uBzYBz3X23mM++\n7v4U4SrxGMLJN58DgUeBXQgbc+VirJeHgF/Fz0cSArSMRfITf18XOMLdz47fX3b3HYg1h2bWlnAQ\n2y0ua4cYeFwKPOTuPYG/FcjPZYST0Q7AuYSgIeNe4Nj4+WjCgTfXkYTAfScWfrPLGHfvQdhZL4kn\nxfWBboQg8Agz+3m+DJlZa8KDwg8C9gTmm1kZcA9wkLvvAnxJuDCoz4nAFHffmXDwuJ0QvPUibDuz\n6pl+EHCtu29PuJDZihK3m5jnu7Py/BphfUM4gf0yro8LCsy7lHEyOsW0dwJ+TXge53ZAdzNbvZ5p\nVzSnAp/FMjuMcBH5sLv3BnoTDsIZC22nmR9zynMVwnFpd0Kg0M3MOuSbsZk1J5wY9or73ew4aF1g\nrLvvQdjH8jUdr+bu+xJaKC5akgVPgSYr2xyDCAHezoTaxPvyHbfiuIWOkWnyE+F42wc4m8LHxUIy\n57xfEAKuvQiB/6oFxs89/+wFrB/Pjz2BS7OOiw+5e69M8Bf9lVCGAEY4lhJ/G0k4F50R5/E0cEE9\n8zgIOBPYtymDP2i8JuBx8f804N/uXmtmU4FV3L3GzOYCD5nZ98D/EWr0ALyedL8BHojTbULW20Ti\n1dyA+PV6wsHgYjM7i/Amktwg6EvgcjObTXg13Yx65l0W/19DCK5eimn8o57psn0BlJnZusCOwOUl\n5GeKu3+bNV7uOtqIcGX1tJkRp92QECzfE8cZDZxmZhsRAjsIV8JGuJLF3ccAY8ysf/z+kZmVm1lH\nwhV0r1hze3Cc/sj4dx2hBuOZrDy9HP+PIQReXwCvu3st8FOsTf1ZznKUxfnONLPfEg4SbYA/xuVr\nDzwal7El8AL1+zmwk5llduCVgJrM+jSzMQWmy5S1Ebcxd38iTtOd0rabNYEZ8SIGQheGawgHkA/i\naxLnxXQws5FAK8LV5WP5ximQR4Bv3f2/MZ0f3P2j+Hk64SSXJkbcFt39YzN7BLg2tgjMYMGxBhbd\nTvOZDbQzs4eA7wllVJdGnn1ihrt/Hb+/Ttg3vgO2NbOeMQ8r55lPpibjC9JXZqVq6rLN2JTYJcnd\n3zWzdQsctzLT5TtGpsk7MQaYTGjFm1DguJgt+/iWOec9Q2jNGUEIKgeY2cGE4ApCJQYsWvb/A7ax\nBf3gWxAumuvSNrMBLGih2g2osNA/99/Aema2LTDd3WfE1rs74rmoBfAx4VxTaB67EbaJoi1WjaGx\nagAL3thgZlsAB7r7ocBZMU+Zwq7JM0kN0MzMViPUdB1GqNmZnTUd7j7K3XvEv6cINY7941VGGeHq\no4YF6+BWoJ+7H0s46WbnoRmherqdmTWPkfz6cfhRwNB4hf9hnM/ieJjQhPxGDIgyiuUnd31k+5Rw\n0tg9XpH8ARhLqCHdPo6zLYC7T8haR/cRNu5tAcxsZzPLbZ64DxgIfOTu09z9tsz0hKb7Qwg1rT2B\n42KwCOHKHEKQ+2GcT/c4nxbADoSd5kdCYAfhrTHEputt3L0PsE+c/zTCTnxAnPfVLNjJixlPuMLr\nQbhqfCTOoyp7vWTnIy5D2/h79vo5Ml5M1LfdZEwB2sTlgVBj/J/4eZH9w933jev2rELjkGd9FRk3\nrbLLbAPC/vCGux9F6IKRfWLJ3U6zZcpzL2Bddz+cUJPUkoWPO9n7xCSgddb21S3+Pw6Y5u5HEvb9\nilhDnE1lWL8mK9usgCWTj51iPrYEJuc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114eeb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用深度为5的决策树进行预测和训练，显示评估指标，显示主要特征的合计重要性，显示按单个特征排序的前五名，导出决策树模型图\n",
    "from sklearn.tree import export_graphviz\n",
    "tree_display = DecisionTreeClassifier(random_state=0, max_depth=5)\n",
    "tree_display.fit(X_train, y_train)\n",
    "tree_pred = tree_display.predict(X_test)\n",
    "print 'Accuracy =', accuracy_score(y_test, tree_pred)\n",
    "print 'F0.5 =', fbeta_score(y_test, tree_pred, 0.5)\n",
    "tree_importance = tree_display.feature_importances_\n",
    "get_importance(tree_importance, 'capital-loss')\n",
    "get_importance(tree_importance, 'capital-gain')\n",
    "get_importance(tree_importance, 'hours-per-week')\n",
    "get_importance(tree_importance, 'education-num')\n",
    "get_importance(tree_importance, 'occupation')\n",
    "get_importance(tree_importance, 'age')\n",
    "get_importance(tree_importance, 'marital-status')\n",
    "vs.feature_plot(tree_importance, X_train, y_train)\n",
    "export_graphviz(tree_display, out_file='tree.dot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/mtyylx/MLND/blob/master/P2_Finding_Donors/tree.png?raw=true\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用OneHotEncoder的模型训练所得到的特征重要性排序，是否会对哪些被拆散的特征不公平？如果使用LabelEncoder是否可以让特征呈现的更清晰？\n",
    "\n",
    "例如occupancy这个特征，按理说工作类型应该是直接和收入相关的，但是却并没有出现在排名中。我认为原因之一是occupancy有十几类，即使整体重要性是10%，被OneHot编码打散后的单个特征的重要性也达不到1%，所以入不了榜。于是我改用LabelEncoder对特征进行处理（代码和结果见下面Cell），同样使用AdaBoost模型进行相同的训练，整体的性能基本一致，F-Score下降了0.7%。但是由于LabelEncoder没有将特征拆分，因此最后训练得到的Feature Importance就有了occupancy和relationship这种之前落选的特征。\n",
    "\n",
    "我理解，这种所谓的不公平只是我主观角度上的想法。特征的排名仅仅是当前特征状态和所选算法的综合产物，换了算法、换了特征的处理方式，最后得到的特征排名将会有很大的变化。毕竟特征选择是为了使用削减特征的训练数据进行更快速的训练这个主旨服务的。因此客观的来看，这种\"不公平\"并不存在。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "富人资产损失大于零的人数： 1098\n",
      "穷人资产损失大于零的人数： 1042\n",
      "富人资产收益大于零的人数： 2375\n",
      "穷人资产收益大于零的人数： 1415\n"
     ]
    },
    {
     "data": {
      "image/png": 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KPXv2pKysjM6dO/PYY4+d93b9/f1JS0tj+vTpvPLKK5w8eZI///nPtG3b9oy+CQkJ57XN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\ndgAAAABgcv8fo33IAhCOyakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf888dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = data['capital-loss'][data['income'] == '>50K']\n",
    "print '富人资产损失大于零的人数：', dist[dist > 0].count()\n",
    "dist2 = data['capital-loss'][data['income'] == '<=50K']\n",
    "print '穷人资产损失大于零的人数：', dist2[dist2 > 0].count()\n",
    "dist3 = data['capital-gain'][data['income'] == '>50K']\n",
    "print '富人资产收益大于零的人数：', dist3[dist3 > 0].count()\n",
    "dist4 = data['capital-gain'][data['income'] == '<=50K']\n",
    "print '穷人资产收益大于零的人数：', dist4[dist4 > 0].count()\n",
    "fig = plt.figure(figsize=(15,7))\n",
    "plt.subplot(221)\n",
    "plt.title(('<capital-loss> distribution for Income > 50K'))\n",
    "plt.hist(dist)\n",
    "plt.subplot(222)\n",
    "plt.title(('<capital-loss> distribution for Income <= 50K'))\n",
    "plt.hist(dist2)\n",
    "plt.subplot(223)\n",
    "plt.title(('<capital-gain> distribution for Income > 50K'))\n",
    "plt.hist(dist3)\n",
    "plt.subplot(224)\n",
    "plt.title(('<capital-gain> distribution for Income <= 50K'))\n",
    "plt.hist(dist4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy = 0.854284134881\n",
      "F0.5 = 0.717840946798\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q0VusYcNGWLVq/Ru1ERKyGb169S6XwNe6dTvs3BkiDN+4cQ3GxiZ48uQx6tY1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\naA5xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10765cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do label encoding for all catgorical features.\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "income = data['income']\n",
    "income = (income == '>50K')\n",
    "\n",
    "cat_features = ['workclass', 'education_level', 'marital-status', 'occupation', \n",
    "           'relationship', 'race', 'sex', 'native-country']\n",
    "num_features = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "\n",
    "trans_feature = {}\n",
    "for f in cat_features:\n",
    "    labels = data[f].values\n",
    "    le = LabelEncoder()\n",
    "    le.fit(labels)\n",
    "    trans = le.transform(data[f])\n",
    "    trans_feature[f] = pd.Series(trans)\n",
    "for f in num_features:\n",
    "    trans_feature[f] = pd.Series(features[f])    \n",
    "trans_feature = pd.DataFrame(trans_feature)\n",
    "\n",
    "X_train_trans, X_test_trans, y_train_trans, y_test_trans = train_test_split(trans_feature, income, test_size=0.2, random_state=0)\n",
    "clf_trans = AdaBoostClassifier(random_state=0)\n",
    "clf_trans.fit(X_train_trans, y_train_trans)\n",
    "y_pred_trans = clf_trans.predict(X_test_trans)\n",
    "print 'Accuracy =', accuracy_score(y_test_trans, y_pred_trans)\n",
    "print 'F0.5 =', fbeta_score(y_test_trans, y_pred_trans, 0.5)\n",
    "\n",
    "importances_trans = clf_trans.feature_importances_\n",
    "\n",
    "vs.feature_plot(importances_trans, X_train_trans, y_train_trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，使用LabelEncoder处理的数据经过同样的训练，得到的特征重要性发生了一些变化，之前从未上榜的occupation变成了第三名。\n",
    "\n",
    "#### 为什么提倡用OneHotEncoder而不是LableEncoder呢？\n",
    "\n",
    "因为LabelEncoder的所做作为会导致潜在的Bias，本身将特征的不同取值规定为0-N就是隐含着偏差的，对于Logistic Regression / Naive Bayes来说，这回导致性能的下降，从上面的测试可以看到，准确度有所下降。\n",
    "\n",
    "而相比之下，OneHotEncoder将特征拆散为有和无的组合，避免了这种对于数值大小的错误偏好倾向出现的可能。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征选择\n",
    "\n",
    "如果我们只是用可用特征的一个子集的话模型表现会怎么样？通过使用更少的特征来训练，在评价指标的角度来看我们的期望是训练和预测的时间会更少。从上面的可视化来看，我们可以看到前五个最重要的特征贡献了数据中**所有**特征中超过一半的重要性。这提示我们可以尝试去*减小特征空间*，并简化模型需要学习的信息。下面代码单元将使用你前面发现的优化模型，并*只使用五个最重要的特征*在相同的训练集上训练模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model trained on full data\n",
      "------\n",
      "Accuracy on testing data: 0.8657\n",
      "F-score on testing data: 0.7415\n",
      "Overal time spend: 7.3069999218\n",
      "\n",
      "Final Model trained on reduced data\n",
      "------\n",
      "Accuracy on testing data: 0.8576\n",
      "F-score on testing data: 0.7312\n",
      "Overal time spend: 5.53699994087\n"
     ]
    }
   ],
   "source": [
    "# 导入克隆模型的功能\n",
    "from sklearn.base import clone\n",
    "\n",
    "# 减小特征空间\n",
    "X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]\n",
    "\n",
    "start = time()\n",
    "# 在前面的网格搜索的基础上训练一个“最好的”模型\n",
    "clf = (clone(best_clf)).fit(X_train_reduced, y_train)\n",
    "\n",
    "# 做一个新的预测\n",
    "reduced_predictions = clf.predict(X_test_reduced)\n",
    "end = time()\n",
    "reduce_time = end - start\n",
    "\n",
    "start = time()\n",
    "clf = (clone(best_clf)).fit(X_train, y_train)\n",
    "org_predictions = clf.predict(X_test)\n",
    "end = time()\n",
    "org_time = end - start\n",
    "\n",
    "\n",
    "# 对于每一个版本的数据汇报最终模型的分数\n",
    "print \"Final Model trained on full data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5))\n",
    "print \"Overal time spend:\", org_time\n",
    "print \"\\nFinal Model trained on reduced data\\n------\"\n",
    "print \"Accuracy on testing data: {:.4f}\".format(accuracy_score(y_test, reduced_predictions))\n",
    "print \"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, reduced_predictions, beta = 0.5))\n",
    "print \"Overal time spend:\", reduce_time\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8 - 特征选择的影响\n",
    "\n",
    "*最终模型在只是用五个特征的数据上和使用所有的特征数据上的F-score和准确率相比怎么样？*  \n",
    "*如果训练时间是一个要考虑的因素，你会考虑使用部分特征的数据作为你的训练集吗？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**\n",
    "\n",
    "可以看到，同样对最终模型进行训练，如果选择对全部特征进行训练，则耗时为7秒，准确率和F-Score为86%以及74%。如果选择只对最重要的5个特征进行训练，则耗时减少2秒，准确率和F-Score都只下降了1%。在实际使用中，如果需要较快的响应，那么可以考虑用部分特征来训练，前提是tradeoff可以容忍，性能下降和运行时间这两个因素可以平衡。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **注意：** 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出**File -> Download as -> HTML (.html)**把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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